COMMENTARY Conducting a National Survey of Women’s Perinatal Experiences in Canada Sampling Challenges
Bibliographic record
Abstract
In 1999, the Canadian Perinatal Surveillance System of Health Canada decided to undertake a national survey of Canadian women’s experiences of their pregnancy, birth and postpartum care. The challenges encountered in selecting a representative sampling frame and developing a sound methodology for conducting a survey of Canadian women at six months after birth are addressed. We considered the advantages and disadvantages of six different sampling options. A sample based on the Census emerged as the optimal approach for providing the most reliable and representative sample. MeSH terms: Health care surveys; women’s health RÉSUMÉ En 1999, les responsables du Système de surveillance périnatale de Santé Canada ont décidé d’entreprendre une enquête nationale sur l’expérience des Canadiennes relativement à leur grossesse, à leur accouchement et aux soins postnatals qu’elles ont reçus. Nous présentons ici les problèmes rencontrés à propos du choix d’une base d’échantillonnage représentative et de l’élaboration d’une méthode valable pour mener une enquête auprès de Canadiennes six mois après leur accouchement. Nous avons tenu compte des avantages et des inconvénients de six formules d’échantillonnage, et celle fondée sur le recensement nous a semblé la meilleure pour obtenir l’échantillon le plus fiable et le plus représentatif. The Canadian Perinatal SurveillanceSystem (CPSS) is a national health sur-veillance program undertaken by the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.086 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.016 | 0.004 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".