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Record W2029660175 · doi:10.2975/28.2005.346.353

Identifying the Core Competencies of Community Support Providers Working with People with Psychiatric Disabilities.

2005· article· en· W2029660175 on OpenAlexaff
Tim Aubry, Robert J. Flynn, Gary Gerber, Theresa Dostaler

Bibliographic record

VenuePsychiatric Rehabilitation Journal · 2005
Typearticle
Languageen
FieldPsychology
TopicPsychiatric care and mental health services
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCard sortingMental healthCore competencyPsychologyTask (project management)Supported employmentMedical educationApplied psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.042
GPT teacher head0.333
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2005
Admission routes1
Has abstractyes

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