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Record W3110628458 · doi:10.1177/0008417420965741

Making Choices from the Choices we have: The Contextual-Embeddedness of Occupational Choice

2020· article· en· W3110628458 on OpenAlexvenueaboutno aff
Karen Whalley Hammell

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyEmbeddednessIdeologySocioeconomic statusOccupational scienceColonialismCitizenshipSociologyPsychologySocial psychologyPoliticsPolitical scienceSocial scienceLawPopulation

Abstract

fetched live from OpenAlex

BACKGROUND.: "Choice" is central to occupational therapy's theoretical tradition, which maintains that individuals can impact their well-being through wisely choosing their occupations. However, the assumption that opportunities to choose are universally available is negated by research evidence. PURPOSE.: To review the ideology of "choice" in occupational therapy theory, and to encourage more critical approaches toward determinants of occupational opportunity and choice. KEY ISSUES.: Evidence indicates that within Canada, and throughout the world, opportunities to make occupational choices are inequitably distributed among people of different socioeconomic classes, castes, genders, races, abilities, sexualities, citizenship statuses, and experiences of colonialism. IMPLICATIONS.: Because occupation is a determinant of health and well-being, social injustices that create inequitable occupational choices are unfair violations of occupational rights. The occupational therapy profession's espoused aim of enhancing well-being through occupation demands theories that explicitly recognize the socially structured and inequitable shaping of choice, and consequent impact on people's occupational rights.

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)0.002
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 score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.508
GPT teacher head0.538
Teacher spread0.030 · 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

Citations41
Published2020
Admission routes2
Has abstractyes

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