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 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".