Issues in Applying a Harm Reduction Approach to Breastfeeding in the Context of Maternal HIV
Bibliographic record
Abstract
To the Editor—A harm reduction approach to breastfeeding in the context of maternal human immunodeficiency virus (HIV) was a refreshing perspective to consider and we congratulate Levinson et al [1] on this provocative suggestion. Harm reduction as a counseling philosophy is in consonance with the World Health Organization guidelines, as both focus on informed parental decision making [2]. Choices regarding infant feeding are highly personalized for parents, and we must not negate that these choices remain personal for women with HIV despite the limitations imposed by transmission risks and national guidelines. As members of a working group devoted to this topic in Ontario, Canada, which organized the referenced forum of >50 women in Toronto, we worry that the application of a harm reduction approach is not an easy task and want to add to this discussion by raising some points that went unmentioned in the original article. We believe that discussions and counseling about infant feeding options in the context of maternal HIV infection are often overlooked entirely, with formula feeding being a foregone conclusion in our setting; this oversight needs to be addressed, first and foremost. We also worry that by neglecting the inherent challenges in implementing a harm reduction approach, further tensions between service providers and community members who advocate breastfeeding for mothers with HIV could arise, and possible harm related to HIV transmission may increase.
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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 | 0.036 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.099 | 0.095 |
| Insufficient payload (model declined to judge) | 0.007 | 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".