Creation of a nationwide breastfeeding policy for surgical residents: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Breast feeding is recommended for the first year of a baby's life due to numerous benefits for both the child and mother. After returning from maternity leave, surgical trainees face extensive barriers to breast feeding and tend to terminate breast feeding earlier than guideline recommendations. The aim of this scoping review is to assess existing breastfeeding policies for surgical trainees at the national level including postgraduate medical education offices, provincial resident unions and individual surgical programmes. METHODS AND ANALYSIS: A modified Arksey and O'Malley (2005) framework will be used. Specifically, (1) identifying the research question/s and (2) relevant studies from electronic databases and grey literature, (3) identifying and (4) selecting studies with independent verification, and (5) collating, summarising, and reporting data while having ongoing consultation between experts throughout the process. Experts will include a lactation consultant (AGB), a human resource leader (JI), a health information specialist (ES), two independent coders (NZ, LR) and a board-certified surgeon (JD). This work will take place as of December 2020 and be carried out to completion in 2021. ETHICS AND DISSEMINATION: Ethics approval will not be sought for this scoping review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.
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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 | 0.131 | 0.090 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.013 |
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".