Optimizing Midwives’ Uptake of a Provincial Perinatal Data System: Lines of Thinking
Bibliographic record
Abstract
In Quebec, the perinatal data available is fragmentary, comes from a number of different databases that are not well integrated, and offers little information regarding the quality of care and services provided by midwives. In 2012, the Ministry of Health and Social Services (MSSS) asked midwives to contribute to the information system on users of local community services centers (I-CLSC). The I-CLSC system is, above all, an administrative monitoring tool; however, it makes it possible to document certain aspects of midwifery practice. Using literature from the fields of knowledge transfer and modification of clinical practices, this article aims to explore under which conditions and to what extent the I-CLSC system could help document midwifery practice. Given the context and the nature of the I-CLSC tool, the success of its uptake by midwives involves the simultaneous reinforcement of its acceptance by midwives, and the provision of support while they use it, so that the data collected is reliable and solid. Training and feedback activities constitute promising avenues in terms of attaining these goals. Literature suggests that, without adequate support, there is a high risk that data fed into the I-CLSC system by midwives will be unreliable. If that is the case, the individual time and effort invested in this system by midwives are unlikely to be cost-effective for either the midwives themselves or midwifery in general.
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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 | 0.114 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.039 | 0.018 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".