Lessons learned about development and assessment of feasibility of tools for health and rehabilitation services
Bibliographic record
Abstract
BACKGROUND: Given the dire need for health and rehabilitation services internationally, exacerbated during the COVID-19 pandemic, there is a critical need to develop tools to support service delivery. This need is palpable in the Global South where tools developed in Eurocentric contexts are not always adaptable, applicable, or relevant. It is for this reason that the researchers present three case studies of tool development using pilot and feasibility studies in South Africa and share the lessons learned from these studies. OBJECTIVES: To describe three case studies that developed new tools for health and rehabilitation services using pilot and feasibility studies. To synthesize lessons learned from these case studies on the development of tools. METHOD: The researchers describe three case studies that were developed. The case studies are summarized as follows: aims and objectives, context, problem, study design, findings, and what happened after the study. Thereafter, a qualitative cross-case analysis was conducted by the researchers to generate themes. FINDINGS: The case studies are described individually and followed by themes identified through cross-case analysis. DISCUSSION: The lessons learned are discussed. It is essential to develop new tools and protocols, motivated by the need for equitable and contextually relevant practices. Partnerships and collaboration with end-users are critical for success. A critical, scientific process is essential in developing new tools. Pilot and feasibility studies are invaluable in developing tools and assessing the feasibility of tools and implementation. The goal is to develop practical, usable tools and protocols. CONCLUSION: Through the lessons learned, the researchers are hopeful that the international health and rehabilitation professions will continue to strengthen the scientific development of contextually relevant tools and resources.
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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 | 0.116 | 0.212 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| 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".