[Missing Data In The Medical Registers Held By The Physicians Of Bamako's Community Health Centres].
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of missing data in the medical registers held by the physicians of Bamako's community health centres, and identify physicians' characteristics associated with the prevalence. METHODOLOGY: A cross-sectional and exploratory study was conducted between January and February 2011. The study population consisted of doctors, and data from their medical consultations. The sample was selected using three-stage sampling. Data were collected through closed-ended questionnaire and record counting. Data analysis was descriptive and analytic. RESULTS: The study involved 32 doctors and data from 3072 medical consultations. Physicians were predominantly male (87.5%). The prevalence of missing data ranged from 0.1% to 95.4% and was higher for diagnoses (15.5%), treatments (17.3%) and observations (95.4%). Missing diagnoses were determined by the number of years in the position, and the number of years of service. Missing treatments were determined by data management training, the number of years in the position and the number of years of service. CONCLUSION: The extent of missing diagnoses, missing treatments and missing observations raise doubts on the quality of health information, effectiveness of health decisions and effectiveness of health interventions.
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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 | 0.012 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".