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Record W4366991434 · doi:10.1080/14473828.2023.2195602

Formative evaluation of an entrepreneurial funding mechanism for training knowledge brokers in occupational therapy relevant research spaces

2023· article· en· W4366991434 on OpenAlexaff
Dianna L. Bosak, Daniel Fulford, Mary A. Khetani

Bibliographic record

VenueWorld Federation of Occupational Therapists Bulletin · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFormative assessmentSituatedMedical educationLogic modelOccupational therapyBusinessPublic relationsPsychologySociologyPedagogyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

We examined how a sponsored contract model (1) produced products of scholarly impact in childhood disability; (2) built scholarly capacity of rising practitioners/scholars in health-related professions; and (3) can be optimized to maximize impact. Data from select lab records and interviews were content analyzed and fitted to the Research Capacity Building (RCB) framework that was situated within the Forging Alliances in Interprofessional Rehabilitation Research (FAIRR) logic model. Traditional outcomes included KT products (53%), followed by publications (16%), presentations (10%), grant submissions (10%), and community research partnerships (10%). Trainees emphasized four professional outcomes including: (1) growing a research network, (2) acquiring research skills, (3) transferring research skills, and (4) assuming leadership roles. Trainees provided multiple suggestions to optimize the contract model. Findings suggest this sponsored contract model yields scholarly products and professional benefits to trainees across multiple backgrounds. Stakeholders could consider increasing leadership opportunities for graduate trainees to maximize impact.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.342
GPT teacher head0.507
Teacher spread0.165 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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