[Interprofessional Collaboration as a Modality to Resolve Therapeutic Impasses in Child Psychiatry: A Review].
Bibliographic record
Abstract
Child and adolescent intervention in child psychiatric clinics generates a high risk of therapeutic impasses for clinicians. Among the factors that contribute to this situation are the increasing severity of the problems of young people who are referred to psychiatric clinics and the obligation for professionals to collaborate with various actors surrounding the patient. This literature review explores the possibility that an intervention targeting indicators of interprofessional collaboration can help resolved the therapeutic impasses encountered by professionals working in child psychiatry. The article begins with a description of the impasse in therapeutic clinical child psychiatry. It then introduces a broad look at research about interprofessional collaboration and its effects on mental health service delivery. Finally, it examines the structuring model of the interprofessional collaboration process of D'Amour et al. in order to highlight the indicators that may be related to the resolution of clinical therapeutic impasses in child psychiatry. This review examines the possible interventions that could be done when targeting indicators of D'Amour et al.'s interprofessional collaboration model in order to improve therapeutic impasses resolution. A promising direction for future research which could contribute to therapeutic impasses resolution in child psychiatry is proposed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".