<scp>eH</scp>ealth versus equity: Using a feminist poststructural framework to explore the influence of perinatal <scp>eH</scp>ealth resources on health equity
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore whether and how eHealth resources targeted to families during the perinatal period effectively reach a diverse population or further oppress marginalised groups. BACKGROUND: eHealth is often intended to reach a broad population, thus health content must be relatively generalised which limits the ability to tailor health education and interventions to individual needs. Generalisation of health information has historically represented a hegemonic depiction of the health consumer, especially within the perinatal period, often disregarding the diversity that exists in the world and perpetuating heteronormative constructs within healthcare systems as a result. DESIGN: A critical review of the literature regarding perinatal eHealth resources was conducted using a feminist poststructuralist approach for analysis. Included literature addresses the development, implementation and/or evaluation of perinatal eHealth resources. DISCUSSION: This approach uncovered hegemonic discourses related to the current state of perinatal eHealth resources. Nurses and midwives have the unique advantage of interacting and understanding diverse populations. Thus, nurses and midwives are integral to the development, implementation and evaluation of eHealth resources to reduce social health inequity. RELEVANCE TO CLINICAL PRACTICE: This paper acts as an exemplar on how to apply feminist poststructuralism to highlight inequities that exist and identifies strategies for nurses and midwives to become involved in the development of eHealth resources or advocate for greater visibility within current resources.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".