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Record W2110226273 · doi:10.2182/cjot.2010.01.77.5.2

Power and Confidence in Professions: Lessons for Occupational Therapy

2010· article· en· W2110226273 on OpenAlexvenueno aff
Florence Clark

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersNational Institute of Nursing Research
KeywordsOccupational therapyPower (physics)MedicineConfidence intervalPsychologyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Powerful professions have the capacity to obtain leadership positions, advocate successfully in the policy arena, and secure the resources necessary to achieve their professional goals. Within the occupational therapy profession, cultivating power and confidence among our practitioners is essential to realize our full capacity for meeting society's occupational needs. PURPOSE AND KEY ISSUES: Drawing from a historical analysis of the medical and nursing professions, this paper discusses the implications of power and disempowerment among health professions for their practitioners, clients, and public image. Theoretical perspectives on power from social psychology, politics, organizational management, and post-structuralism are introduced and their relevance to the profession of occupational therapy is examined. IMPLICATIONS: The paper concludes with recommendations for occupational therapy practitioners to analyze their individual sources of power and evaluate opportunities to develop confidence and secure power for their professional work--in venues both in and outside the workplace.

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)0.010
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 score0.027
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.041
Scholarly communication0.0140.019
Open science0.0020.008
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0080.001

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 head0.380
GPT teacher head0.559
Teacher spread0.180 · 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

Citations40
Published2010
Admission routes1
Has abstractyes

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