Registered nurse lactation consultants' experiences supporting maternal mental health: A qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: The province of Nova Scotia has the highest rates of perinatal mental health disorders in Canada, and rates of exclusive breastfeeding fall below the Canadian national average. Exclusive breastfeeding is identified as a protective factor against the development of perinatal mental health disorders. Lactation consultant support is associated with increased rates of exclusive breastfeeding and decreased rates of perinatal mental health disorders. Despite this, little is known regarding the experiences of Registered Nurse Lactation Consultants related to supporting maternal mental health. OBJECTIVE: To understand the experiences of Registered Nurse Lactation Consultants related to supporting maternal mental health. DESIGN: A qualitative descriptive design using online semi-structured interviews. SETTING & PARTICIPANTS: Ten Registered Nurse Lactation Consultants employed in the publicly funded healthcare system in Nova Scotia, Canada, were recruited via purposive sampling. FINDINGS: Three themes emerged regarding the relational experiences of Registered Nurse Lactation Consultants while supporting maternal mental health; these included (1) Experiences supporting maternal mental health, (2) Providing maternal mental health care, and (3) Mothers need support. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Registered Nurse Lactation Consultants described positive experiences supporting maternal mental health and indicated that lactation consultant appointments were an opportune time to provide screening and support for maternal mental health. Enhancing support for maternal mental health requires collaborative and integrated approaches throughout the perinatal period. Healthcare providers, including Registered Nurse Lactation Consultants, must be provided with the support and resources to provide timely and appropriate support for maternal mental health throughout the perinatal period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".