Public health nurses as social mediators navigating discourses with new mothers
Bibliographic record
Abstract
Public health nurses (PHN) have had a long history of working with new mothers in the community. Their practice includes collaboration, building therapeutic relationships, mutual goal setting, establishing trust, supporting clients' strengths, empowerment and social justice. The wealth of information that new mothers receive both solicited and unsolicited may come from many different sources such as medicine, midwifery and those created personally by families. Although much of the information on mothering is presented with the intent of helping, it can also be hegemonic and oppressive depending on different discourses, stereotypes and myths of mothering and therefore may cause confusion, guilt and uncertainty. Public health nurses often address conflicting social, cultural and personal discourses about mothering practices in order to support an empowering mothering experience. The term 'social mediator' was purposefully created in an attempt to describe the unique work of PHNs that this author has witnessed through her own research and practice as a PHN. This paper will present a discussion of the author's own work and research findings that will suggest how feminist poststructuralist theory may be used to guide and understand information exchange between PHNs and mothers as they mediate different social, cultural and personal discourses on mothering.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".