Reliability of reasons for early termination of breastfeeding: Application of a bivariate probability model with sample selection to data from surveys in Malaysia in 1976–77 and 1988–89
Bibliographic record
Abstract
Although extensively collected, data on people's reasons for their behaviour provided retrospectively have been met with some scepticism on the grounds that they may be subject to biases and errors that jeopardize their usefulness. This study investigates, for a sample of 1,327 births, the reliability with which women in Peninsular Malaysia recalled, at intervals 12 years apart, reasons for not initiating or for stopping breastfeeding less than 3 months after a birth. Overall, we find low to moderate reliability of recall. Levels of reliability are relatively high for some reasons (the child died and no or insufficient milk) but low for some others (child ill, breastfeeding inconvenient). Results from selection models show that reliability does not vary with the length of time since the child's birth but is inversely related to socio-economic status (proxied by education and employment). Social status, social norms, and health-related factors appear to be significant influences on women's consistency of reporting.
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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 | 0.082 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".