Dissatisfaction with the laboratory services in conducting HIV related testing among public and private medical personnel in Tanzania
Bibliographic record
Abstract
BACKGROUND: A comprehensive care and treatment program requires a well functioning laboratory services. We assessed satisfaction of medical personnel to the laboratory services to guide process of quality improvement of the services. METHODOLOGY: A cross-sectional survey in 24 randomly selected health facilities in Mainland Tanzania was conducted to assess the satisfaction of the medical personnel with the laboratory services. RESULTS: Of 235 medical personnel interviewed, 196 were valid for analysis and about one quarter were dissatisfied with the laboratory services. Personnel dissatisfied with the services were 38.3% in timely test result, 24.5% in correct and accurate results and 22.4% in clear complete results. The personnel in public laboratories were more dissatisfied with timely test results (OR = 3.6, 95% CI 1.8, 7.3), correct results (OR = 4.1, 95% CI 1.6, 10.8) and clear complete results (OR = 5.0 95% CI 1.6, 15.2). Personnel dissatisfied with the services in 15 laboratories sending specimens to referral laboratories, varied from 13% in availability of equipment to 57% in timely results feedback from the referral laboratories. Personnel dissatisfied with the services in 14 referral laboratories, varied from 28.6% in properly identified specimen to 42.9% in clear, accurate test request and communication. CONCLUSION: About one quarter of medical personnel in sending or receiving laboratories were dissatisfied with the services. Comparing the personnel in public and private, the personnel in public laboratories were 4 times more dissatisfied with the timely test and correct results; and 5 times more dissatisfied with clear and complete test results.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".