Southern Online Journal of Nursing Research
www.snrs.org
Issue
2, Vol. 4
Knowledge Structures and Problem Representations: How Do Novice and Expert Home Care Nurses Compare?
Jo Azzarello, RN, PhD
Assistant Professor, University of Oklahoma College of Nursing, Oklahoma City, OK
ABSTRACT There is a growing need for home care nurses
expert in quickly developing an accurate conceptualization of complex
patient situations. An understanding of the cognitive processes underlying
this expert skill would provide a foundation for optimal training and
practice experiences for the development of expert home care nurses.
However, few studies have investigated problem solving within complex
situations faced by home care nurses. The purposes of this study were
to compare novice and expert home care nurses’ underlying knowledge
structures and determine their influence on pattern recognition and
ability to draw inferences from complex patient data. Specific aims
of the study were to: 1) compare novice and expert home care nurses’
knowledge structures and problem representations, and 2) determine
if underlying knowledge structures predict pattern recognition. This
descriptive exploratory study used a written question answering task
to measure domain knowledge structures, and a think-aloud question
answering task to measure pattern recognition and inferences for a
written patient scenario in five novices and five experts in home care
nursing. Findings reveal both similarities and differences in experts’
and novices’ underlying knowledge structures and representations. Knowledge
structures were more predictive of pattern recognition for experts
than for novices. Implications include mentoring periods for inexperienced
home care nurses, employee assessment, utility for nursing education,
and future research. Keywords: Knowledge Structures, Problem
Representation, Pattern Recognition, Inference, Home Care Nursing,
Problems
Introduction Home care nursing practice is particularly
challenging because home care nurses routinely face problem situations
in which a patient has a broad range of multiple interacting medical,
functional, and behavioral problems affecting current and future health
status. Beyond assisting patients to achieve goals related to restoration,
rehabilitation, and palliative care, the home care nurse strives to
promote patient or caregiver competence and judgment in the independent
management of health care needs at home.1 The resulting problem solving situation
is complex for several reasons. There is often limited availability
of information about the total situation, with only a portion of the
variables (e.g., physical signs) lending themselves to direct observation.
This requires the nurse to infer additional information about the patient’s
underlying condition and situation. Also, complex patient situations
frequently have a high degree of connectivity of variables. That is,
changes in one problem can affect the status of other co-existing problems.
Additional difficulties can arise when some of the goals and interventions
for concurrent problems are contradictory. Recent changes in the home
care industry, such as increasing patient acuity and the introduction
of a prospective payment system, complicate the situation. There is
a dramatic and growing need for home care nurses expert in quickly
developing an accurate conceptualization of complex patient situations
so that comprehensive, effective care can be delivered. An understanding
of the cognitive processes underlying this expert skill would provide
a foundation for planning optimal training and practice experiences
for the development of expert home care nurses and serve as the basis
for assessments aimed at identifying levels of expertise. Although some recent studies have begun
to investigate problem solving within the complex situations that home
care nurses face,2-4 little
remains known about the similarities and differences between novice
and expert home care nurses’ underlying structure of domain knowledge,
or their recognition of patterns and generation of inferences when
representing multidimensional patient situations. Of particular concern
are questions of how organization of the underlying knowledge base
relates to problem representation for problems involving multiple interacting
problem entities in home care nursing. The purposes of this study were to compare
novice and expert home care nurses’ underlying knowledge structures
and determine their influence on pattern recognition and ability to
draw inferences from patient data. Specific aims were to: 1) compare
novice and expert home care nurses on underlying knowledge structures
and problem representation; and 2) determine if underlying knowledge
structures predict pattern recognition by novice and expert home care
nurses.
Conceptual Basis and Literature Review Theoretical foundations of domain specific
problem solving were established from studies conducted in knowledge-rich,
non-nursing domains using novice-expert comparisons.5-13 From these studies, characteristics of experts’
problem representation and underlying knowledge structure were identified. Problem
Representation. Domain specific problem solving is the process
through which an individual determines solution procedures based
on previous knowledge and experience in a specific content area.
Prior to determining a solution, an individual develops a representation,
or internal model, that consists of elements within the problem,
the relationships among them, and inferences drawn from the knowledge
base of the solver.5,6 Much
of an expert’s problem solving power lies in the ability to quickly
establish correspondence between externally presented events and
internal models of these events through pattern recognition and inferred
relations that define the situation.7,8 Research comparing novices and experts
on problem solving performance in physics,9 chess,10 and
medicine11 shows that experts are
able to focus on meaningful patterns in the information, avoiding attention
to the irrelevant details of problems in their domain. In addition,
experts are able to use available problem data to infer added information
for problem solving. Experts demonstrated superior ability to identify
relevant cues or important features of the problem at hand when compared
to novices.12 However, other studies
have found that novices’ problem solving difficulties generally did
not stem from failure to identify relevant cues, but from limited ability
to abstract the relevant elements in the problem data.11,13 While
both novices and experts were able to pick out relevant problem features,
novices were less likely to generate inferences and relations not explicitly
stated in the problem. A limited number of studies on problem
representation in nursing specifically examine pattern recognition
and inference. Benner’s work on skill acquisition in nursing is congruent
with findings from other disciplines regarding experts’ superior skill
in recognizing patterns in patient condition, including very subtle
changes, compared to novices.14 In
a study of wound care decisions, experts focused more on problem-specific
(meaningful) data than novices.15 In
a study of decision making in third-space fluid shift problems, more
experienced nurses used more cues, selectively clustered these cues,
and made more accurate inferences than nurses with less experience.16 However, no differences were found between nursing
students and experienced staff nurses in the number of inferences generated
in a simulated diagnostic reasoning task.17 Knowledge
Structure. An interconnected,
organized knowledge base appears to underlie experts’ ability to
accurately detect relevant patterns in problem data and to infer
additional relations and constraints from the situation.6,18 The organization and interconnections among declarative
facts within the learner’s knowledge network is referred to as structural
knowledge.19-21 Knowledge
of how concepts within a domain are interrelated is believed to contribute
to the automaticity and speed of accurate problem solving seen with
expert performance. The shift from storing knowledge as isolated
facts and loosely bundled units of information to highly integrated
knowledge structures is a key factor that characterizes the transition
from novice to expert.22 Novices’ underlying knowledge
is less complete, less interconnected, and contains more erroneous
information than that of experts. In addition, experts appear to
know more about the appropriate application of their knowledge, with
declarative knowledge tightly bound to conditions for its use.18,23,24 Broderick and Ammentorp examined knowledge
structures of nursing students and licensed nurses by comparing how
subjects sorted data elements related to a patient scenario into categories.25 The researchers found no
differences between the knowledge structures of the two groups. Lauri
and Salantera found that the degree of abstraction of knowledge structure
was positively related to creative decision making in nursing.26 However, the researchers did not compare novice and
expert nurses. Previous studies have been successful in
identifying some aspects of knowledge structures and problem representation
that differentiate novices and experts. However, the majority of these
studies have concentrated on problems that are dissimilar to those
encountered in home care nursing. Much of what is known about expert
problem representation and knowledge structures is found in research
using single dimensional problems (e.g., solving a physics problem
for which there is one correct answer, identifying a single medical
diagnosis, making wound care decisions) rather than using complex multidimensional
problems such as those typically faced by home care nurses (e.g., multiple
medical and functional problems interacting within a single patient). Methods This descriptive exploratory study used
a two group, novice/expert comparison design. The study was approved
by the University of Oklahoma Institutional Review Board and confidentiality
of participants was maintained. Sample
and Setting. A total
of ten nurses participated in the study: five novices and five experts
in home care nursing. Novices were randomly chosen from an Oklahoma
State Board of Nursing list of recent graduates from baccalaureate
programs for registered nurses (RN) who had graduated within the
month prior to data collection and successfully tested for licensure.
Length of experience as an RN ranged from 2 weeks to 1 month. None
was previously licensed as an RN or Practical Nurse, or had prior
work experience in home health care in any capacity (e.g., home health
aide). Four were female, and one was male. Experts were identified
through networking with the author’s industry contacts and members
of the Oklahoma Home Care Association. All experts were female and had a minimum
of a baccalaureate degree in nursing. Two expert participants had a
master’s degree in nursing and a third expert was certified in home
care nursing through the American Nurses Credentialing Center. Length
of experience as an RN ranged from 6 to 24 years. Home care experience
ranged from 5 to 13 years. Supervisors or peers rated each expert as
having superior knowledge and skill in home health nursing practice. Participants were contacted by telephone,
given a brief explanation of the study, and scheduled for an appointment
to meet individually for data collection. At the meeting, an explanation
of the study was again provided and signed written consent obtained. Variables
and Measurement. Knowledge
structure is the interrelationships among domain related concepts
within an individual’s knowledge base. For the purposes of this study,
variables related to interrelationships and concepts were operationalized
in the following way. Six concepts defining the domain of home care
nursing (congestive heart failure [CHF], depression, impaired mobility,
poor medication management, falls, and poor nutrition/hydration)
and two types of relationships or links among concepts (“characteristic
of” and “leads to”) were used in measures of both knowledge structure
and problem representation. The six concepts were chosen by the researcher
from a review of home care nursing literature due to their relevance
for home care nursing, ability to be represented as a problem or
potential problem to be addressed by the nurse, and the high degree
of interrelationship among them. The “characteristic of” and “leads to”
relationships among concepts were chosen because interpretations of
data characteristic of the patient’s condition and inferences of underlying
causal dynamics were found to be particularly relevant in studies of
problem conceptualization in health care.6,11 A
“characteristic of” link is one that indicates one concept is a feature,
attribute, or characteristic of another concept. A “leads to” link
is one that indicates a causal relationship between concepts. It should
be noted that individuals usually do not store ideal representations
of causal mechanisms, but rather only fragments of the true cause-oriented
mechanisms.27 Therefore, the “leads to”
relationship does not indicate a strict causal relationship in the
sense that one set of events or states constitutes a necessary and
sufficient cause of another event or state. The “leads to” link is
used in a more general sense to indicate that one concept leads to,
causes, or results in another concept. Procedures. The
researcher remained present throughout all data collection procedures.
First, data measuring participants’ underlying domain specific knowledge
structures were collected using a written question answering task similar
to that used by Graesser and Clark27 in
their studies of knowledge structures and discourse processing. Participants
were presented with each of the six home care concepts printed at the
top of a page and asked to write their answers to two questions related
to each of those concepts: (1) What characteristics might patients
exhibit that would indicate they have a problem with [concept]? (2)
What are the causes of [concept]? There was no time limit for completion. After completion of the above, problem
representation data were collected using a think-aloud question answering
task similar to that used by Patel, Evans, and Kaufman28 in their study of medical diagnosis and reasoning.
Problem representation is the ability to analyze data in a problem
situation and work out a conceptualization of the problem. Of interest
to the present study was participants’ ability to recognize patterns
in the data and to make inferences during representation when presented
with a patient scenario containing multiple interacting problems or
potential problems. The six study concepts formed the basis of a realistic
patient scenario that included facts characteristic of the concepts
and which implied a causal relationship among them, as well as irrelevant
data. History of congestive heart failure was explicitly stated in
the data; all other problem concepts and the relationships among them
were not explicitly stated but could be inferred from the given information.
Although facts were given in the scenario that are characteristic of
current problems with depression, impaired mobility, poor medication
management, and poor nutrition/hydration status, no facts were given
that indicated a problem with falls had occurred yet. That is, there
were no facts to imply that the patient had actually incurred a fall,
but the situation was such that without appropriate intervention a
fall was likely to happen. Participants were allowed three minutes
to read the case scenario and were not allowed to refer back to the
printed problem during the exercise. Inability to refer back to the
scenario forced increased reliance on underlying knowledge structures
during the formation of a problem representation and enhanced expert
and novice differences in the representation process. After reading the scenario, participants
wrote on a blank piece of paper what problems should be addressed by
the home care nurse. After completing the list, participants answered
aloud two questions for each problem identified: (1) How do you know
the patient has a problem with [listed problem]? (2) What do you think
is causing the problem with [listed problem]? When answering the questions,
participants stated aloud everything they were thinking. Participants’
verbal responses (protocols) were tape recorded for later analysis.
The identified problems (pattern recognition) and verbal protocols
(inference) constituted participants’ representation of the problem. Data
Analysis. Participants’
written responses to questions testing knowledge structures were
analyzed according to (1) number of statements in response to questions;
(2) correct and incorrect “characteristic of” statements; (3) correct
and incorrect causal statements; and (4) interconnectivity among
the six study concepts. Interconnectivity was a measure of the number
and percentage of connections between two primary concepts. A connection
was considered to exist if a characteristic or cause listed for one
of the concepts was: (1) also listed as a characteristic or cause
of another of the six concepts (e.g., lack of knowledge listed as
a cause of both poor medication management and poor nutrition), or
(2) was another of the six concepts (e.g., impaired mobility listed
as a cause of depression). Classifications and scoring rules were
adapted from those used by Graesser and Clark.27 Analysis was conducted by two registered
nurses who were knowledgeable and experienced in home care nursing.
Each rater was trained in the classification procedures and interrater
agreement was 98%. Both raters had to agree in order for a statement
to be categorized; disagreements were determined by the researcher.
Means, standard deviations, and proportions for both groups were compared. Participants’ written list of problems
and verbal protocols in the representation task were segmented, transcribed,
and analyzed in a manner similar to that used by Patel, Evans, and
Kaufman.28 Data were translated into
conceptual graphs that modeled participants’ problem representations,
consisting of nodes interrelated by a network of directed relational
links. A node is an idea or concept listed as a problem to be addressed
by the participants or stated as a characteristic or cause during the
think aloud exercise. Nodes were either facts given in the patient
scenario or inferences derived from the scenario. Relational links
represented the relationships between nodes and were directional. Links
were classified as “characteristic of” if one node was a feature, attribute,
or characteristic of the second node, or as “leads to” if one node
was noted to lead to, cause, or result in the second node. Conceptual graphs were used to examine
similarities and differences in the two groups’ representation, pattern
recognition, inference, and cohesion. Cohesion of the problem representation,
or the degree of interconnection perceived among the scenario patient’s
problems and associated data and inferences, was determined by calculating:
(1) the mean number of relational links per problem node (problem density),
and (2) number of causal associations directly linking individual problems,
reflecting the degree to which participants viewed each problem as
interacting with and affecting each other. In addition, participants’
pattern recognition and inference generation were qualitatively analyzed
as to content, scenario information used in the identification of problems,
errors, and recognition of potential problems. Participants’ responses to the written question answering task (knowledge structure measure) were used in a simple prediction model to determine whether underlying knowledge structures could be used to predict specific problems identified from the patient scenario (problem representation measure). In this model, similar to that developed by Gordon and Gill,29 it was assumed that when data in the case scenario mapped onto information that a participant had associated with one of the study’s six concepts in the knowledge structure task, that concept would be included during problem representation as a problem to be addressed. Conversely, if a participant did not identify associations with one of the six study concepts similar to those in the case scenario, it was predicted that the participant would not identify a problem associated with that concept during the problem representation task. Predictions were visually compared to participants’ actual problem representations to test for accuracy of the model. Possible outcomes are described in Table 1. ______________________________________________________________________________ Table
1. Possible Prediction Model
Outcomes
The + + (predicted inclusion of concept,
actual inclusion of concept) and - - (predicted exclusion of concept,
actual exclusion of concept) outcomes represent accuracy of the prediction
model (hits). Hits indicate accuracy in predications. The + - (predicted
inclusion of concept, actual exclusion of concept) and - + (predicted
exclusion of concept, actual inclusion of concept) outcomes represent
errors in the prediction model (misses). Misses indicate inaccuracy
or errors in predications. Finally, the relationship between interconnections
among underlying knowledge structures and associations in problem representations
for novices and experts was examined using Spearman’s rank-order correlations. Results A summary of the differences
between novice and expert knowledge structures and problem representation
is provided in Table 2. Knowledge
Structures. Experts
listed a greater number of both characteristic statements (M = 62.8, SD = 18.52) and causal statements (M = 49.6, SD = 17.35) than
did novices (M = 36.2, SD = 9.24 and M=29.0, SD = 5.06, respectively),
although experts and novices listed the same proportion of characteristic
statements (56%) to causal statements (44%). There were only slight
differences in the accuracy of statements between the two groups.
Of the 314 total characteristic statements listed by experts, 95%
were correct, while 93% of the novices’ 181 total characteristic
statements were correct. Experts listed a total of 248 causal statements
with a 96% accuracy rate, while novices listed a total of 145 causal
statements, with a 97% accuracy rate. Experts’ statements were more
interconnected than those of novices. Of the 562 total statements
made by experts, 170 (30%) were connected to each other. For novices,
75 (23%) of the 326 total statements were connected to each other. Experts and novices made several qualitatively
similar types of statements. Both noted factors that could be verified
only through laboratory testing, such as “anemia,” “decreased serum
albumin,” and “digoxin level high.” Also, both groups stated many and
varied outward signs or objective observations that might be characteristic
of patients with problems related to the concepts, for example “edema,”
“unsteady gait,” and “poor skin turgor.” Experts’ and novices’ statements also exhibited
qualitative differences. Experts listed a greater number and variety
of statements that related to social and environmental characteristics
and causes than did the novices, for example “lack of social support,”
“low income,” and “multiple environmental hazards.” Experts also noted
a greater number and variety of symptoms or subjective information
about patients that would be known or verified only through history
taking, questioning, or interviewing patients or their caregivers,
such as “confusion from too many meds,” and “decreased appetite.” Problem
Representation. Visual
inspection of all experts’ representations showed data and inferences
that were so interrelated as to present a singular web of problems.
Three of the novices’ representations, however, consisted of two
to four concurrent yet unrelated groups of problems that appeared
as separate islands within the total representation. In contrast,
representations of the experts were highly interrelated. Average
problem density for experts’ graphs was 6.30 compared to 4.44 for
novices. Experts noted a mean of 5.6 causal associations directly
linking individual problems, while novices noted a mean of 1.4. Experts
were able to offer extensive explanations of the problems, while
novices’ graphs were less extensive and generally contained less
satisfactory explanations. For example, all experts were able to
state possible causes of each of the problems identified, yet two
of the novices were unable to speculate any possible cause for one
of the problems they identified. Pattern recognition was reflected in the problems identified from data in the scenario. Novices identified a range of three to seven problems to be addressed (M = 4.8) while experts identified from four to seven (M = 5.6). All of the experts noted the potential for exacerbation of CHF as a problem to be addressed, while none of the novices did even though the scenario specifically stated a history of CHF and included evidence of noncompliance with medications. However, only two of the experts noted a potential for falls while all of the novices did. The only other potential problem cited was by one expert who noted the potential for skin breakdown. Experts made a total of 111 inferences from scenario data (M = 22.2), while novices made 79 (M = 15.8). There were 13 total inferential errors made by novices (11%), and 85% of those were related to causal inferences. Only one inferential error (characteristic) was made by the experts.
Prediction of Problem Representation from Knowledge
Structure. The prediction
model was more accurate in predicting inclusion or exclusion of specific
problems in the representation for the experts (79%) than for the
novices (59%). Hits were almost exclusively due to the “+ +” type
for both groups. There were twice as many misses for novices (12)
than for experts (6). Almost all of the experts’ misses were “+ -,”
one each for four of the experts while the fifth had none. However,
misses for the novices were evenly split between “+ -“ and “- +”
types. Two of the novices had one miss each, while the remaining
three novices had from 2 to 5 misses each. Correlation between total
interconnectivity of underlying structural knowledge and representation
density was rs = .40 for both novices and experts. A closer look
at the correlation between just the causal links in underlying knowledge
structure and representations revealed differences between the groups.
Findings were rs =
.23 for novices and rs =
.45 for experts.
Discussion Consistent with research in other fields,
expert home care nurses’ underlying structural knowledge was more extensive
and interconnected than that of novices. However, unlike findings in
some domains, novices and experts were very similar in the accuracy
of their underlying knowledge so differences were not related to erroneous
knowledge elements. While both groups’ underlying structures were
very similar in some types of knowledge associated with the six study
concepts, there were notable differences. Experts appeared much more
knowledgeable about social and environmental factors, as well as subjective
information that could be elicited to evaluate them. Both of these
differences are likely related to the experts’ extensive practice in
the home care setting, where there is considerable focus on social
and environmental factors that impact patients’ and caregivers’ ability
to manage care. Through their extensive practice, experts appeared
to have developed broad, highly interconnected knowledge structures
related to concepts relevant to home care practice. Basic nursing education
programs have traditionally focused students’ limited practical experiences
on hospitalized patients, where novice nurses have less opportunity
to develop structures related to the impact of home environment and
social support on self- or caregiver-management. Findings regarding problem representation
were also similar to those from other fields and to those using single
dimensional problems. Experts’ problem representations were more complete,
complex, and cohesive, indicating a highly dynamic conceptualization
of the patient situation. The present study’s use of multidimensional
problems also demonstrates that experts are more likely than novices
to view individual sub-problems within a single patient as being highly
interrelated with and affecting each other, particularly in a causal
manner. Experts’ problem conceptualizations included a greater
number of problems with a high degree of interconnectivity. Novices’
representations were somewhat more superficial and piecemeal than those
of the experts. There were too few potential problems in the scenario
to closely examine recognition of potential problems from patterns
in the data. However, experts did demonstrate a distinct superiority
compared to novices in ability to recognize the potential for exacerbation
of CHF as an area that should be addressed. Findings related to identifying
other potential problems (e.g., falls) were mixed, and warrant additional
study. In research conducted in other domains,
there are conflicting findings regarding novices’ ability to attend
to relevant cues in the problem data. In some studies, novices experienced
difficulty in identifying relevant cues, while in other work novices
were very similar to experts in identifying key elements. In the present
study, both novices and experts cited the same scenario data to support
identification of problems. However, while novices and experts used
similar cues in the case scenarios to develop their representations,
novices at times used inaccurate reasoning chains, leading to inferential
errors from the data. Experts stated problems as broad concepts
(e.g., “poor nutrition” and “depression”), demonstrating ability to
view given signs and symptoms as subcomponents of higher level problems.
In contrast, novices more often stated problems on a lower level than
experts, for example, “need to gain weight.” Many of the problems listed
by novices were considered by experts to be symptoms of broader problems.
For example, one novice listed “ambulating with use of wall” as a problem,
while experts listed it as a sign characteristic of a problem with
mobility. What was particularly evident, however, was novices’ greater
likelihood of inferring different meanings to some cues or to make
no inference at all. Novices identified many of the same occurring
problems as the experts based on detection of patterns in the data.
However, at other times novices either did not identify a problem,
or viewed it in a less complete manner or at a superficial depth of
understanding. It appeared that limitations in novices’ pattern recognition
were associated with limits in ability to consistently generate the
appropriate inferences and relations not explicitly stated in the problem. In earlier studies of problem solving cognition
it was noted that when individuals’ underlying knowledge structures
contained incomplete associations, those individuals were less apt
to develop complete problem representations.23 However,
the present study’s findings are more consistent with (1) the presence
of weak, rather than absent, knowledge links, and (2) less understanding
of when to apply specific knowledge, as explanations for novices’ less
complete problem representations. If missing links among concepts within
knowledge structures were the primary explanation, a greater number
of prediction model hits would be of the “- -“ type. That is, underlying
associations in structural knowledge would be absent, so no association
would be made during problem presentation. However, since there were
very few “- -“ hits for either group (one for experts, two for novices),
this is not likely. It also seems reasonable to assume that
strong links would result in consistent ability to associate concepts
during both free recall and problem representation, as seen with the
experts, and that weaker links would result in inconsistent ability
to associate concepts during free recall and problem representation,
as seen with novices. In addition, experts may know more about the
appropriate conditions for application of their knowledge, increasing
the predictability of its use. In contrast, novices may have less knowledge
related to conditions of applicability, therefore decreasing the predictability
that their underlying knowledge will be applied in specific problem
situations. The moderate positive relationship between
total interconnectivity of structural knowledge and cohesion of problem
representation is consistent with research in other fields. Differences
between experts and novices in the relationship between causal interconnections
in knowledge structures and causally interacting patient problems in
the representations were specifically examined because in practice,
changes in one patient problem can cause changes in other coexisting
problems. As might be expected, there was a stronger relationship for
experts than for novices, although, due to small sample size, significance
could not be demonstrated. Differences related to knowledge of conditions
of applicability and stronger links among knowledge elements could
account for this finding as well. Limitations. The number of participants in this study is
small which limits generalizability of the findings. Readers must therefore
take this limitation into account when interpreting findings. The traditional method or criteria for
determining expertise levels is to base expertise level on the amount
of experience in the domain of interest. This may not be the most differentiating
factor possible. Attempts were made to compensate for this by considering
subjective evaluations of participants by those familiar with their
knowledge and expertise. However, there is still the possibility that
all persons included in the study were not truly representative of
their expertise level. The literature reflects some concern that
completion of a structural knowledge task may affect subsequent problem
representation if the question answering task used to measure structural
knowledge uses the same concepts included in the problem representation
scenario.20 Although
there was no evidence of question probe intrusiveness in a study using
a similar approach,29 there
was no feasible way to check for this effect in the present study.
Some researchers20 suggest
inserting additional concepts among those of interest in order to decrease
the likelihood of question probe intrusiveness. This was tried during
pilot testing of the present study’s materials; however, doing so considerably
lengthened participation time. Participants reported fatigue after
completing the lengthy task which may have affected performance on
the problem representation task. While the possibility of intrusiveness
remains, the researcher felt it would pose a lesser threat to study
validity than participant fatigue. Implications
for practice, education, and research. Generalization of study findings is limited due
to small sample size, however several implications warrant examination
in future studies. New graduates or nurses with no experience in
home care may not be prepared to assume a fully independent role
in establishing the initial care plan for home care patients. Lack
of experience in home care practice appears related to less knowledge
regarding certain aspects of care for patients at home, particularly
related to social support and environmental influences. Inexperience also appears related to the development
of patient representations that may be superficial and less dynamic,
with some errors in reasoning. Until the nurse obtains adequate experience
in home care practice, it may be advisable to encourage close preceptor
and mentoring activities with coworkers who are expert home care
nurses. Also, employee assessment tests that focus on underlying
knowledge alone may not be adequate in predicting the ability of
nurses with no home care experience to develop comprehensive patient
representations. The assessment of knowledge structures
and problem representation may hold some utility for nursing education.
This study and others30 document
the influence of extensive practice in a problem solving domain for
the development of expert performance. Therefore, it is reasonable
to expect that nursing student clinical experiences in community settings
may influence their underlying knowledge structures or representations
for complex home care nursing patients. More information is needed regarding the
extensiveness and specific types of home care experiences under which
knowledge structures expand and/or strengthen and problem representation
skill begins to approach expert levels. Studies with representative
samples and variety in problem situations are needed to generate more
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