Doctors’ perspectives on their innovations in daily practice: implications for knowledge building in health care
Bibliographic record
Abstract
CONTEXT: When individuals adapt their practice in order to solve novel or unexpected problems of practice, they are creating new knowledge. This form of innovation development is understood as a core competency of adaptive expertise and the basis for knowledge building community practice. However, little is known about the ways in which this knowledge, produced through daily, innovative problem solving, is developed, identified and shared by health care professionals. METHODS: Following this line of inquiry, we conducted semi-structured interviews with a saturation sample of 15 clinical faculty staff at the University of Toronto. RESULTS: A grounded theory analysis of the results showed that our participants held the view that innovation was focused on outcomes, developed through research practice and diffused for adoption in the broader community. As a result, their own individual improvements to daily practice were excluded from this view of innovation. Furthermore, their perceptions of innovation limited participants' engagement in the sort of collaborative process that is central to the practice of knowledge-building communities. CONCLUSIONS: This research demonstrated that thinking about innovation and innovative practice must be changed in order to foster the development of knowledge-building communities in medicine.
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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 | 0.030 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".