2277- PROFESSIONAL COMPETENCIES FOR ACCOMPANYING CHANGE IN HEALTH DOMAIN
Bibliographic record
Abstract
Major changes different domains in Quebec (Canada) and other country in the last decade provided the backdrop for accompaniment-research-training projects. In two projects already done Lafortune (2008a,b; 2012a,b) identified two structured, foundational models (education and health domains) to implement major changes that require an overhaul of professional practices and greater professional autonomy; and to develop expertise in accompaniment-training in using reflective-interactive strategies. The approach taken, accompaniment-research-training, extends data collection so that the research instruments serve as training tools and vice versa. Another feature is the adaptation and reuse of materials, which leads to evidence of actions taken. Additionally, accompaniment-research-training is characterized by emergent theory building. Among the outcomes of the project described herein are a model for change and a framework of eight professional competencies for accompanying a change. This text focuses on the frame of reference of eight competencies, which constitutes an integrated, coherent system that includes descriptions of the competencies and professional acts used to accompany and implement change. Two competencies will be presented: C2: Model reflective practice when accompanying change; C3: Take the affective domain into consideration when accompanying change. (Lafortune, 2008a; Lafortune, 2012b).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".