Lessons learned from an occupational therapy programme needs assessment
Bibliographic record
Abstract
Background: A needs assessment identifies the differences between actual and ideal situations to facilitate the development of a new programme or improve existing services. Objectives: This article shares our experiences conducting the needs assessment in a context where people had limited or no understanding of the need being assessed. Method: Adhering to a three-phase model – comprising pre-assessment, assessment, and post-assessment – we employed diverse data collection methods, including quantitative survey, qualitative interviews, and environmental scan. Results: The findings underscored the necessity of expanding rehabilitation services in Ethiopia, with a shift from a purely medical focus to addressing issues associated with daily functioning and community engagement. These issues align closely with the core expertise and responsibilities of occupational therapists. Participants expressed support for the introduction of occupational therapy in Ethiopia and willingness to incorporate the practice of occupational therapists in their settings. The challenges encountered were how to ask about occupational therapy when it is not well known by members of the local population and how to introduce the profession without biasing participants’ responses. Conclusion: Conducting a needs assessment was critical to developing occupational therapy services in Ethiopia. We welcome others to learn from our experiences. Contribution: This manuscript details the assessment process and delves into the challenges we encountered and lessons learned. It extends methodological suggestions to inform future evaluations and contributes valuable insights to the broader discourse on needs assessment and programme development in a context where people have limited awareness of services, such as occupational therapy.
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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 | 0.080 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".