Réflexion critique sur les clientèles difficiles et leur accès aux services psychosociaux
Bibliographic record
Abstract
Le concept de clientèles difficiles a été peu étudié et sa définition ne fait pas consensus. Or, les clientèles difficiles présentent des caractéristiques communes et soulèvent de nombreux défis pour les professionnels. En effet, ces clientèles sont susceptibles d’engendrer des impasses thérapeutiques et des réactions contre-transférentielles massives et la relation thérapeutique peut s’en voir affecter. De plus, le manque de services spécialisés pour répondre aux besoins particuliers de ces clientèles génère des difficultés pour les professionnels qui interviennent auprès d’elles. La collaboration interdisciplinaire s’avère ainsi essentielle. Cet article propose de discuter des différents enjeux associés aux clientèles difficiles et à leur accès aux services psychosociaux.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".