Perinatal depression screening in community pharmacy: Exploring pharmacists’ roles, training and resource needs using content analysis
Bibliographic record
Abstract
BACKGROUND: Perinatal depression (PND) screening is often recommended in primary care settings, which includes the community pharmacy setting. However, there is limited research exploring pharmacists' perspectives on their roles in screening for perinatal mental illness. AIM: This study aimed to explore pharmacists' views of pharmacists' roles in PND screening, as well as training and resource needs for PND screening in community pharmacy settings. METHOD: A questionnaire including three open-ended questions focusing on pharmacists' perspectives of their role in PND screening, their training, and resource needs in this area, was disseminated to pharmacists across Australia via professional organisations and social media. Each open-ended question was separately analysed by inductive content analysis. Subcategories were deductively mapped to the Theoretical Framework of Acceptability. RESULTS: Responses (N = 149) from the first open-ended question about pharmacists' roles in PND screening resulted in three categories (PND screening in primary care settings will support the community, community pharmacy environment, and system and policy changes) and ten subcategories. Responses to question two on training needs (n = 148) were categorised as: training content, training length, and training delivery while responses about resource needs (n = 147) fell into three categories: adapting community pharmacy operating structures, pharmacist-specific resources, and consumer-specific resources. CONCLUSION: While some pharmacists were accepting of a role in PND screening due to pharmacists' accessibility and positive relationships with consumers, others had concerns regarding whether PND screening was within pharmacists' scope of practice. Further training and resources are needed to facilitate pharmacists' roles in PND screening, referral and care.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".