Participatory Methods to Develop Health Education for PW-SCI: Perspectives on Occupational Justice
Bibliographic record
Abstract
Background. Many people with spinal cord injury (PW-SCI) in the Gaza Strip in Palestine are discharged from inpatient rehabilitation with limitations in their ability to meet basic needs, and reach their full potential. There is limited evidence of how clinicians can promote occupational justice for PW-SCI. Purpose. To describe participants’ perspectives revealed during a participatory action research (PAR) process used to develop an education manual for PW-SCI in Gaza, using Participatory Occupational Justice as a lens. Methods. Following ethical approval, a four-step PAR design was utilized by eight researchers to co-construct the Spinal Cord Injury Activities of Daily Living—education Manual with 54 participants from SCI rehabilitation settings in Gaza. Qualitative data from eight focus groups were analyzed using inductive thematic analysis. Findings. Two main themes were evident in the participants’ viewpoints: Enabling occupational justice and Removing barriers to occupational justice. Implications. Occupational justice is a central value that needs to be considered when developing occupational therapy educational interventions for this client group. PW-SCI health education may facilitate occupational justice in practical and culturally relevant ways when participatory methods are used to develop educational 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 | 0.143 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".