Facilitators and challenges in partnership research aimed at improving social inclusion of persons with disabilities
Bibliographic record
Abstract
PURPOSE: To identify partnership research challenges and facilitators, as experienced by members of the Inclusive Society (IS) initiative. MATERIALS AND METHODS: A case study was conducted on all partnership research projects conducted between 2017 and 2019 under the IS initiative through surveys, interviews with the IS community, logbooks, and focus group. Thematic analysis and descriptive analysis were undertaken. RESULTS: To work effectively with a diversity of stakeholders, winning conditions must be created for the project from the outset. These include determining the team functioning, project objectives, the expectations of each party, and agreeing on a realistic action plan. Project implementation with concern for sustained stakeholder commitment, good working relationships, and achieving project objectives requires organizational planning that favours partner involvement, shared leadership, agreed methods for communicating, conflict resolution methods, recognition of each participant's expertise, and creating a climate of trust. Upon concluding a partnership research project, it is essential to devote time to implement project results in local environments and to ascertain their usefulness to partners. IS partnership research challenges and facilitators are similar to those identified in past research. Despite this knowledge, challenges persist. Future research could explore tools and practices from other domain to overcome partnership research challenges.
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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 | 0.141 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".