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Group Decision Making in an Intersectoral Mental Health Community Partnership

2012· article· en· W2100154008 on OpenAlexafffundabout
Mari Basiletti, Elizabeth Townsend

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2012
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Prince Edward IslandDalhousie University
FundersCanadian Occupational Therapy Foundation
KeywordsFocus groupGovernment (linguistics)General partnershipJurisdictionMental healthQualitative researchOccupational therapyPsychologyNursingPublic relationsMedicineBusinessPolitical sciencePsychiatrySociologyMarketing

Abstract

fetched live from OpenAlex

Background: In a major Canadian jurisdiction that includes several regions, an intersectoral working group with community partners was formed to enable change in systems and policies for individuals with serious and persistent mental illness. The top priority of the working group was housing. Purpose: The study explored how the working group members experienced decision-making power in their efforts to enable change in housing policies. Method: The research used a qualitative single-case design to study the decision-making processes as experienced by the group members. Data were collected through individual semi-structured interviews, two focus groups and review of key public documents. The data were analysed using the constant comparative method, with critical reflection on group decision making and the contextual influences of system-level policies. Group members contributed to the analysis. Findings: Amid positive experiences of working together, group members experienced challenges related to power differentials between service providers, government personnel and consumers, and the impact of the systemic environment on the group processes. Implications: Implications are raised for occupational therapy education and practice, and for studying group decision making as an occupation.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.335
GPT teacher head0.546
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2012
Admission routes3
Has abstractyes

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