Identifying the Core Competencies of Community Support Providers Working with People with Psychiatric Disabilities.
Bibliographic record
Abstract
The study was intended to identify core competencies for community support providers working with people with psychiatric disabilities. Using multiple methods developed from previous research in the field of developmental disabilities, 18 consumers receiving services and 16 staff members from two mental health community support programs identified a list of 68 competencies that included personal attributes, knowledge, and skills. Based on a card sort task, 34 consumers receiving services and 34 support workers from six mental health community support programs rated 59 of the 68 competencies as being either absolutely necessary or desirable. Results of a second card sort task found that a majority of competencies identified as being needed pre-employment were personal attributes consistent with adopting a person-centered approach. Competencies categorized as to be learned on the job involved special knowledge and skills specific to working with people with psychiatric disabilities. The range of personal attributes, knowledge, skills represented in the identified competencies reflects the complexity of contemporary mental health community support. Findings are indicative of the need for specialized training and supervision that has not been typically available in the community mental health sector.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".