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Record W3085891147 · doi:10.1111/phn.12807

Public health nurses’ experiences learning and delivering a group cognitive behavioral therapy intervention for postpartum depression

2020· article· en· W3085891147 on OpenAlexaff
Haley Layton, Daniella Bendo, Bahar Amani, Peter Bieling, Ryan J. Van Lieshout

Bibliographic record

VenuePublic Health Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsWestern UniversityMcMaster UniversityThe King's UniversityImpact
Fundersnot available
KeywordsIntervention (counseling)NursingThematic analysisPublic healthPublic health nursingQualitative researchPsychologyMental healthMedicineCognitive behavioral therapyCognitionClinical psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: Public Health Nurses (PHNs) often provide support to women with postpartum depression (PPD) in the absence of specialized training. The objective of this study was to explore the experiences of six PHNs who were trained to deliver a group cognitive behavioral therapy (CBT) intervention for PPD in the public health setting, and to describe how learning and delivering this intervention affected their professional roles and personal lives. DESIGN: This qualitative study employed a phenomenological approach. SAMPLE: Six PHNs who completed the CBT training program and delivered at least one CBT group in their community. MEASUREMENTS: Individual in-depth interviews were conducted and transcribed verbatim. Transcripts were analyzed according to thematic derivation procedures. RESULTS: The themes that emerged from the interviews with the PHNs included: (a) components of the CBT training program that nurses most valued, (b) benefits of training for their professional role as a PHN, (c) implications for practice, and (d) using CBT skills in their personal lives. CONCLUSIONS: The provision of CBT training to PHNs may not only positively impact their work with clients with mental illness, but may also have the potential to provide broader clinical and professional benefits for these skilled professionals and their other clients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.143
GPT teacher head0.416
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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