Estimating the stochastic uncertainty underlying sample-based estimates of infant mortality in the Philippines: a first-time application to a country in the Southeast Asia/Pacific Basin region
Why this work is in the frame
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Bibliographic record
Abstract
Infant mortality is an important population health statistic that is often used to make health policy decisions. Unfortunately, these data are not available for all populations. A newly developed method is presented for accounting for the stochastic uncertainty found in infant mortality rates (IMRs) estimated from sample surveys and for the first time applied to a country in the Southeast Asian/Pacific Basin area, the Philippines. The method is founded on the fact that there are two sources of variation in sample-based estimates of IMRs: (1) sample size; and (2) variation of infant deaths. The approach is aimed at taking into account stochastic uncertainty while preserving information concerning the uncertainty due to sampling. In applying the method to the Philippines, the sample-based IMR estimates appear to perform well in terms of accounting for stochastic uncertainty. This finding is consistent with previous research assessing this approach in Africa and with variations, in Canada, Europe and the United States, which suggests that in the form presented here or in one of its variants, it could successfully be employed not only elsewhere in the Southeast Asia/Pacific Basin region but also in East Asia, North Asia, South Asia, and West Asia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it