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Record W2048466711 · doi:10.15453/2168-6408.1055

Choosing the Path of Leadership in Occupational Therapy

2014· article· en· W2048466711 on OpenAlexaffabout
Clark Patrick Heard

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsOccupational therapyMentorshipQualitative researchFocus groupLeadership studiesInterpretative phenomenological analysisPsychologyLeadership developmentMedical educationNursingLeadership styleMedicinePublic relationsSociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Leadership is vital to the success and sustainability of any group, organization, or profession. Using a qualitative phenomenological methodology, consistent with interpretative phenomenological analysis, this study examines why occupational therapists choose the path of leadership. Data was collected through the completion of semistructured interviews with 10 occupational therapy leaders in Ontario, Canada. This collected data was transcribed verbatim and coded for themes by multiple coders. Several methods were employed to establish trustworthiness. Results identify that a desire to influence the profession or care delivery, a need for personal or career development, and a need for change motivate those occupational therapists who might choose the path of leadership. Recommendations for supporting new or developing leaders include a focus on linking occupational therapy practice and leadership theory at the curriculum and professional levels. Moreover, application of novel approaches to mentorship for new and developing leaders, such as supportive communities of practice, are also considered.

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)0.009
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.010
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.640
GPT teacher head0.556
Teacher spread0.084 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations23
Published2014
Admission routes2
Has abstractyes

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