Formative evaluation of an entrepreneurial funding mechanism for training knowledge brokers in occupational therapy relevant research spaces
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.144 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".