Bibliographic record
Abstract
The common factors hypothesis holds that therapy's effectiveness is not found in features of specific therapeutic methods, but rather that therapy is helpful due to the presence of three general features that all therapeutic modalities share: positive clinician traits, commitment to a common practice to achieve a common goal and effective communication. These features are also cited as crucial competencies of doctors in general. Yet despite the demonstrable importance of these ‘soft skills’, prominent clinical educators are calling for greater understanding and implementation of common factors education. In this paper, I seek to provide an account of what a ‘common factor’ or ‘soft skill’ is, and how to incorporate education of these ‘common factors’ into clinical education. To this end, I argue that common factors are best understood as philosophically informed reflective practice. Current medical accounts of therapy and the education of therapeutic practice lack this understanding, and as a result miss some key conceptual features that philosophically informed reflective practice could provide. I will also demonstrate how philosophically informed reflective practice can be suffused into familiar clinical pedagogical methods. In doing so, I will illustrate how clinical education can be reformed with philosophically informed reflective practice in mind.
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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 | 0.048 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.096 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".