Expanding the Haitian rehabilitation workforce: employment situation and perceptions of graduates from three rehabilitation technician training programs
Bibliographic record
Abstract
PURPOSE: This article examines the employment situation and perceptions of graduates from three rehabilitation technician (RT) programs in Haiti. METHODS: In this mixed method study, 74 of 93 recent graduates completed a questionnaire, and 20 graduates participated in an in-depth qualitative interview. We analyzed survey results using descriptive statistics. We used a qualitative description approach and analyzed the interviews using constant comparative techniques. RESULTS: Of the 48 survey respondents who had completed their training more than six months prior to completing the questionnaire, 30 had found work in the rehabilitation sector. Most of these technicians were working in hospitals in urban settings and the patient population they treated most frequently were patients with neurological conditions. Through the interviews, we explored the participants' motivations for becoming a RT, reflections on the training program, process of finding work, current employment, and plans for the future. An analysis of qualitative and quantitative findings provides insights regarding challenges, including availability of supervision for graduated RTs and the process of seeking remunerated work. CONCLUSIONS: This study highlights the need for stakeholders to further engage with issues related to formal recognition of RT training, expectations for supervision of RTs, concerns for the precariousness of their employment, and uncertainty about their professional futures. Implications for Rehabilitation The availability of human resources in the rehabilitation field in Haiti has increased with the implementation of three RT training programs over the past 10 years. RTs who found work in the rehabilitation sector were more likely to work in a hospital setting, in the province where their training had taken place, to treat a diverse patient clientele, and to be employed by a non-governmental organization. The study underlines challenges related to the long-term sustainability of RT training programs, as well as the employment of their graduates. Further discussion and research are needed to identify feasible and effective mechanisms to provide supervision for RTs within the Haitian healthcare system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".