Perinatal Women’s Views of Pharmacist-Delivered Perinatal Depression Screening: A Qualitative Study
Bibliographic record
Abstract
Internationally, 20% of women experience perinatal depression (PND). Healthcare providers including general practitioners and midwives are critical in providing PND screening and support; however, the current workforce is unable to meet growing demands for PND care. As accessible and trusted primary healthcare professionals, pharmacists could provide PND care to complement existing services, thereby contributing to early detection and intervention. This study aimed to explore perinatal women’s views of community pharmacist-delivered PND screening and care, with a focus on their attitudes towards and acceptability of PND screening implementation in community pharmacy. Semi-structured interviews with women (n = 41) were undertaken, whereby interview data were transcribed verbatim and then inductively and thematically analysed. Five overarching themes emerged; “patient experience with existing PND support and screening services”; “familiarity with pharmacists’ roles”; “pharmacist visibility in PND screening care”; “patient—pharmacist relationships” and “factors influencing service accessibility”. Themes and subthemes were mapped to the Consolidated Framework for Implementation Research. Findings highlight participants’ generally positive attitudes towards community pharmacist-delivered PND screening and care, and the potential acceptability of such services provided pharmacists are trained and referral pathways are established. Addressing perceived barriers and facilitators would allow community pharmacist-delivered PND screening and care to support existing PND care models.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".