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Record W3184019454 · doi:10.1177/00084174211022891

Occupational Therapists as Social Change Agents: Exploring Factors that Influence Their Actions

2021· article· en· W3184019454 on OpenAlexafffundvenueabout
Jessica Picotin, Michael F. Beaudoin, Sandrine Hélie, Ann-Élisabeth Martin, Annie Carrier

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité de Sherbrooke
FundersFaculty of Medicine and Health, University of SydneyYoung Researchers and Elite ClubUniversité de Sherbrooke
KeywordsOccupational therapyThematic analysisPsychologyApplied psychologyPopulationFocus groupQualitative researchNursingMedicineEnvironmental healthBusiness

Abstract

fetched live from OpenAlex

BACKGROUND.: When acting effectively in their social change agent (SCA) role, occupational therapists can impact population health and occupational justice. However, empirical evidence of the influence of personal and environmental factors on their ability to act as SCAs is scarce. PURPOSE.: To explore personal and environmental factors that influence the ability of occupational therapists to act as effective SCAs. METHOD.: We conducted a descriptive interpretive qualitative study with 18 Québec occupational therapists recognized as successful SCAs. We collected data through semi-structured interviews with three focus groups and analyzed them thematically using a lexicon. FINDINGS.: We identified nine cross-cutting personal factors, including discovery, effective communication, and planning, that enable occupational therapists to act as successful SCAs. Six thematic groups of environmental factors facilitated or hindered their actions. IMPLICATIONS.: To act effectively as SCAs, occupational therapists need to consider personal and environmental factors involved in their change project.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.754
GPT teacher head0.553
Teacher spread0.201 · 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

Citations20
Published2021
Admission routes4
Has abstractyes

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