Les facteurs influençant le sentiment d’efficacité personnelle des enseignants travaillant auprès d’élèves ayant un trouble du spectre de l’autisme : analyse de quatre entretiens menés auprès de professionnels enseignants exerçant au sein du dispositif spécifique Autorégulation
Bibliographic record
Abstract
The ambition of a fully inclusive school and the success of all the pupils is an ever present goal for all primary teachers. However, when the special need of a student is accompanied by important behaviour difficulties as for example can be characterized by troubles of the spectre of autism, the issue concerning school learning is sometimes given second place for the benefit of an action centred more on coping with the trouble and development of social interactions. The autoregulation scheme as developed by Stéphane Beaulne,a clinician researcher and professor at the University of Nipissing(Canada),is designed to help educational teams to act following a double ambition :a)allow autistic students to develop autoregulation competences and b)help professionals to develop the required competences to practice their job with the latter. Consequently, we have interviewed four primary teachers who work with autistic pupils and have analysed what influenced their sense of efficacy within this scheme. This research will prove, among other things, that the characteristics of a training based on advanced expertise and on support by exchanges are a source of a solidified sense of efficacy. The specific characteristics of the scheme, allowing multidisciplinarity and social support among colleagues working in the same place, will equally be a factor of development.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".