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Record W2107996153 · doi:10.7870/cjcmh-2007-0028

Health Promotion and Community Development: An application of Occupational Therapy in Primary Health Care

2007· article· en· W2107996153 on OpenAlexaffvenueabout
Barry Trentham, Lynn Cockburn, Jennifer L. Shin

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsRegent Park Community Health CentreUniversity of Toronto
Fundersnot available
KeywordsHealth promotionOccupational health nursingOccupational therapyPromotion (chess)Mental healthCharterCommunity developmentPsychological interventionNursingMedicinePublic relationsPsychologyPublic healthPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

This paper shares the experiences of 3 occupational therapists whose work has been guided by health promotion and mental health promotion principles while using community development strategies. Although health promotion has been lauded for over 2 decades as a useful model from which to practice community occupational therapy, few illustrations of its application to practice exist. The Ottawa Charter for Health Promotion is reviewed as a starting point from which to frame mental health promotion and community development strategies. Two group programs are presented to illustrate an occupational therapy application of the health promotion model and community development strategies within a primary health care setting. Lessons learned from the therapists' work are shared in terms of conceptual understandings of community occupations, consumer barriers to participation, sustainability of community development projects, and balancing individual client issues with community-level interventions. The programs exemplify the role of occupational therapy in the development of community participation opportunities for interested individuals.

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)0.017
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.206
GPT teacher head0.518
Teacher spread0.312 · 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

Citations30
Published2007
Admission routes3
Has abstractyes

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