Agir en tant qu’agent de changement : effets longitudinaux d’une formation sur mesure visant à développer les compétences des professionnelles des services sociaux et de santé
Bibliographic record
Abstract
Background: We aimed to describe the immediate and medium-term effects of training in the role of change agent (CA) on: 1) the perceived competence to act as a CA; 2) the acquisition of skills required for the role; and 3) the anticipated and actual implementation of CA actions in real-world settings by professionals. Methods: Using a summative evaluation design and a self-administered online questionnaire, we collected data at three time points: before the training, immediately after, and six months later. We analyzed the data using descriptive and inferential statistics. Results: The 39 participants, aged 25 to 44, were mostly women. For both perceived competence to act as a CA and the acquisition of the required skills, the proportion of professionals responding positively increased significantly immediately after the training and decreased six months later. Although professionals intended to act as CAs right after the training, their actions had not materialized within the following six months. Conclusion: Support measures may help sustain the effects of the training in the medium term and encourage real-world implementation of change agent actions.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".