“Entrenched practices and other biases”: unpacking the historical, economic, professional, and social resistance to de-implementation
Bibliographic record
Abstract
BACKGROUND: In their article on "Evidence-based de-implementation for contradicted, unproven, and aspiring healthcare practices," Prasad and Ioannidis (IS 9:1, 2014) referred to extra-scientific "entrenched practices and other biases" that hinder evidence-based de-implementation. DISCUSSION: Using the case example of the de-implementation of radical mastectomy, we disaggregated "entrenched practices and other biases" and analyzed the historical, economic, professional, and social forces that presented resistance to de-implementation. We found that these extra-scientific factors operated to sustain a commitment to radical mastectomy, even after the evidence slated the procedure for de-implementation, because the factors holding radical mastectomy in place were beyond the control of individual clinicians. We propose to expand de-implementation theory through the inclusion of extra-scientific factors. If the outcome to which we aim is appropriate and timely de-implementation, social scientific analysis will illuminate the context within which the healthcare practitioner practices and, in doing so, facilitate de-implementation by pointing to avenues that lead to systems change. The implications of our analysis lead us to contend that intervening in the broader context in which clinicians work--the social, political, and economic realms--rather than focusing on healthcare professionals' behavior, may indeed be a fruitful approach to effect change.
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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 | 0.321 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.127 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".