Changing clinicians' habits: Is this the hidden challenge to increasing best practices?
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to reflect on the concept of habit as an under-explored, but critically important factor that might help explain the lack of uptake of new, scientifically sound practices by rehabilitation clinicians. METHOD: The complexity relating to being a scholarly practitioner is first presented. The transtheoretical model of behaviour change, developed to better understand behaviour change such as stopping a 'bad' habit or implementing a 'good' one for health improvement purposes, is used to foster reflection on factors involved in uptake of best practices in rehabilitation. To illustrate simply the different scenarios relating to uptake of best practices, such as the use of a standardised tool over a home-grown one, two well known approaches to assessment (use of thermometer versus hand on forehead) that could be used to assess the same construct (body temperature) are contrasted. RESULTS: As rehabilitation clinicians, we are potentially blocked in our uptake of best practices by our habits. Although habits are often comfortable, and change is less so, we need to move away from our comfort zone if we are to adopt best practices. CONCLUSIONS: Given the extensive literature suggesting that there are major gaps between best practice and actual practices, it behoves us to explore the impact of habits to a greater extent.
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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 | 0.078 | 0.237 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".