Perceptions of Rehabilitation Coordinators on Health Information System for Rehabilitation Services in KwaZulu-Natal
Bibliographic record
Abstract
BACKGROUND: A health information system (HIS) is crucial for the planning and management of health services. A HIS provides evidence for programme and policy decisions to ensure better population health outcomes. A HIS aims to improve data collection and handling to extract valuable information thus providing quality health services. Rehabilitation therapists in health facilities in KwaZulu-Natal (KZN) collect data for the monitoring and evaluation of rehabilitation services. Rehabilitation departments in different health facilities have designed data collection tools that are suitable for their institutions, resulting in inconsistency in what is collected across the province of KZN. The study seeks to explore the perceptions of rehabilitation coordinators concerning appropriate indicators for planning and monitoring rehabilitation services in health facilities in KZN. METHODS: An exploratory qualitative approach was used. Data was collected through face-to-face in-depth interviews with rehabilitation coordinators who were employed in provincial and district offices in the KZN Department of Health in 2018. Interviews were conducted from June 2018 through September 2018. Each interview was audio-recorded and transcribed verbatim. A thematic analysis was implemented. RESULTS: The participants highlighted that community outreach, access to multiple assistive devices and discipline specific indicators are appropriate for monitoring rehabilitation services. In addition, integration of rehabilitation indicators with priority programmes such as HIV and TB should be considered. CONCLUSION: Rehabilitation coordinators conceptualise the current indicators as limited and insufficient. Rehabilitation indicators should be have linkages with other programmes and reflect the multiple disciplines that fall within rehabilitation services.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".