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Record W2785281697 · doi:10.1177/0008417417734831

Opportunities for well-being: The right to occupational engagement

2017· article· en· W2785281697 on OpenAlexvenueaboutno aff
Karen Whalley Hammell

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersLiander
KeywordsOccupational therapyTheme (computing)PsychologyEngineering ethicsMedicinePublic relationsPolitical sciencePsychiatryComputer scienceEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The Canadian Association of Occupational Therapists' 2017 conference theme prompted thoughts about shaping our profession's future. PURPOSE: This Muriel Driver Memorial Lecture explores how occupational therapy's future might be shaped to become more important, relevant, and valuable to society. KEY ISSUES: Because occupational engagement is integral to human well-being and because well-being is integral to human rights, occupational therapy could usefully advance the right of all people to engage in occupations that contribute positively to their own well-being and the well-being of their communities. IMPLICATIONS: Occupational therapy's importance to society will be manifested when we focus unambiguously on well-being; extend our efforts beyond enhancing the abilities of individuals whose lives are already impacted by illness, injury, or impairment; and address the opportunities for achieving well-being through occupational engagement of all those whose capabilities-their opportunities to do what they have the abilities to do-are inequitably constrained.

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)0.015
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.085
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0190.055
Scholarly communication0.0140.008
Open science0.0020.019
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0180.003

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 head0.557
GPT teacher head0.543
Teacher spread0.013 · 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 designTheoretical or conceptual
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

Citations109
Published2017
Admission routes2
Has abstractyes

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