Determinants of physicians’ intention to collect data exhaustively in registries: an exploratory study in Bamako’s community health centres
Bibliographic record
Abstract
BACKGROUND: The incomplete collection of health data is a prevalent problem in healthcare systems around the world, especially in developing countries. Missing data hinders progress in population health and perpetuates inefficiencies in healthcare systems. OBJECTIVE: This study aims to identify the factors that predict the intention of physicians practicing in community health centres of Bamako, Mali, to collect data exhaustively in medical registries. DESIGN: A cross sectional study. METHOD: In January and February 2011, we conducted a study with a random sample of thirty two physicians practicing in community health centres of Bamako, using a questionnaire. Data was analyzed by using descriptive statistics, correlations and linear regression. MAIN OUTCOMES MEASURES: Trained investigators administered a questionnaire measuring physicians' sociodemographic and professional characteristics as well as constructs from the Theory of Planned Behaviour. RESULTS: Our results showed that physicians' intention to collect data exhaustively is influenced by subjective norms and by the physician's number of years in practice. CONCLUSIONS: the results of this study could be used as a guide for health workers and decision makers to improve the quality of health information collected in community health centers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".