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Record W2314130664 · doi:10.1177/0008417414536712

Social occupational therapy

2014· article· en· W2314130664 on OpenAlexvenueno aff
Ana Paula Serrata Malfitano, Roseli Esquerdo Lopes, Lílian Magalhães, Elizabeth Townsend

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapySocial injusticeInjusticeGeneral partnershipSocial workSocial exclusionOccupational sciencePublic relationsMedicineSociologyPsychologyPolitical scienceSocial psychologyPsychiatryPolitics

Abstract

fetched live from OpenAlex

BACKGROUND: Researchers and practitioners worldwide have advocated for the development of critical perspectives in occupational therapy to examine the structural influences of social exclusion and injustice experienced by individuals, groups, and communities. To take action against social exclusion and injustice, Brazilian occupational therapists have been developing "social occupational therapy," referring to practice that is focused on social issues and funded outside the health system. PURPOSE: This paper presents a Brazilian perspective on the concept and practice of social occupational therapy. Illustrations are drawn from 12 studies, developed between 2008 and 2013, which were completed with socially vulnerable youth through an ongoing university-community engagement partnership in São Carlos, São Paulo State, Brazil. KEY ISSUES: The authors discuss possibilities and challenges for developing a socially committed, transformative occupational therapy outside the health system. IMPLICATIONS: Occupational therapists may wish to seize opportunities to address social issues and attract funding beyond health services.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0530.009

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 head0.438
GPT teacher head0.550
Teacher spread0.111 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2014
Admission routes1
Has abstractyes

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