Supporting psychiatric mental health nurse practitioners’ preparedness to treat mental health concerns during pregnancy: Results from a grounded theory study
Bibliographic record
Abstract
Background and objective: Pregnant persons are less likely to be screened and treated for depression and anxiety during pregnancy compared to the pre- and post-natal periods, despite adverse effects associated with untreated mental health concerns during pregnancy. Patients have reported that maternal and mental health providers seem unable or unwilling to discuss treatment with psychopharmacological options during pregnancy. Literature concerning this pattern has not included the perspective of psychiatric mental health nurse practitioners (PMHNP). The objective of this study was to identify the barriers and needs of PMHNPs regarding the treatment of mental health concerns during pregnancy.Methods: In this constructivist grounded theory study, data were collected between February 2023 and February 2024 through in-depth interview. Eligible participants were PMHNPs, or PMHNP students, working with patients who might become pregnant in an outpatient setting.Results: Seventeen PMHNPs or students participated in this study. Many believed they were unprepared to treat pregnant patients and described barriers and needs that impede their comfort and willingness to treat people who are pregnant. These included inadequate training, limited research, and concerns about legal liability. PMHNPs requested more information about perinatal mental health and its treatment to be incorporated into training programs and clinical experience.Conclusions: Many PMHNPs were unaware or underinformed of available resources and best practices for treatment during pregnancy. In addition to best practices for the treatment of people who are pregnant, PMHNP programs should consider including preparation for the emotional consequences of practice as well as clear and accurate information about malpractice and liability risks.
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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 | 0.078 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".