Agreement between Staff and Service Users concerning the Clientele's Mental Health Needs: A Quebec Study
Bibliographic record
Abstract
Objectives: This article examines the differences found between clientele with severe mental health problems and their key health workers in terms of assessing service users' needs in 6 Quebec service areas. Method: We questioned 165 pairs of users and staff, using the Camberwell Assessment of Needs questionnaire. The profile of serious and overall problems encountered by clientele from each of the sites was compared. Results: The sites with the greatest degree of user–staff agreement in identifying problems were also the ones where users considered that local services best met their needs. Conclusions: The study demonstrated that, in needs assessment, major differences exist between the perceptions of users and their key workers in the various sites. These differences can be explained in part by users' individual characteristics, by types of needs, by local particularities, and by service use. Objectifs: Cet article examine les différences décelées entre la clientèle souffrant de graves problèmes mentaux et ses principaux travailleurs de la santé, pour ce qui est de l'évaluation des besoins des utilisateurs de services, dans 6 réseaux de services du Québec. Méthode: Nous avons interrogé 165 paires d'utilisateurs et d'employés, à l'aide du questionnaire d'évaluation des besoins de Camberwell. Le profil des problèmes sérieux et généraux éprouvés par la clientèle de chaque site a été comparé. Résultats: Les sites ayant le niveau d'entente le plus élevé entre utilisateurs et employés dans l'identification des problèmes étaient aussi ceux où les utilisateurs estimaient que les services locaux répondaient au mieux à leurs besoins. Conclusions: L'étude a démontré qu'en matière d'évaluation des besoins, il existe des différences majeures entre les perceptions des utilisateurs et celles de leurs principaux travailleurs, à divers sites. Ces différences peuvent s'expliquer en partie par les caractéristiques individuelles des utilisateurs, par les types de besoins, par les particularités locales et par l'utilisation des services.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".