Exploring the Gap Between Knowledge and Behavior: A Qualitative Study of Clinician Action Following an Educational Intervention
Bibliographic record
Abstract
PURPOSE: Many medical education interventions improve clinicians' knowledge but fail to change behavior. The authors exposed this knowledge-behavior gap through standardized clinical interactions, thus allowing in-depth exploration of the contributing factors. METHOD: A typical evidence-based educational intervention in one clinical domain (early signs of autism) was administered to family medicine residents at the University of Toronto in 2001-02, and change in knowledge was assessed through a multiple-choice test. Six to eight weeks later, participants' relevant knowledge was documented, and their clinical behavior was observed during four interactions with standardized patients. Factors producing a knowledge-behavior discrepancy were then explored using semistructured interviews, which were audiotaped, transcribed, and analyzed using grounded theory methods. RESULTS: Half of participants demonstrated varying degrees of knowledge-behavior gap. Eight main rationalizations (relationships, patient agenda, knowledge deficit, clinical style, means to an end, ideals, autism stigma, and systems barriers) were used to justify choices of clinical behavior, and the same rationalizations were used to justify opposite choices of behavior. Two conditions that promote clinical action based on knowledge (level of certainty and sense of urgency) were identified. CONCLUSION: The knowledge-behavior gap was exposed and factors reported to influence clinicians' decisions about whether to implement new knowledge were elicited. That identical rationalizations were used to justify opposite behaviors implies these factors may not be behavioral determinants. Sense of urgency and level of certainty promote clinical action based on knowledge; focusing on these may increase the impact of education on practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".