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Record W2736845113 · doi:10.1186/s12884-017-1431-4

Approaches to health-care provider education and professional development in perinatal depression: a systematic review

2017· review· en· W2736845113 on OpenAlexafffund
Laura Legere, Katherine Wallace, Angela Bowen, Karen McQueen, Phyllis Montgomery, Marilyn Evans

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2017
Typereview
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsLaurentian UniversityLakehead UniversityUniversity of SaskatchewanWestern UniversityRegistered Nurses' Association of Ontario
FundersOntario Ministry of Health and Long-Term CareRegistered Nurses' Association of Ontario
KeywordsMedicineMental healthInclusion (mineral)Reproductive medicineNursingProfessional developmentHealth careDepression (economics)Systematic reviewAntenatal depressionFamily medicineMEDLINEPsychiatryMedical educationPsychologyPregnancyAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: Perinatal depression is the most common mental illness experienced by pregnant and postpartum women, yet it is often under-detected and under-treated. Some researchers suggest this may be partly influenced by a lack of education and professional development on perinatal depression among health-care providers, which can negatively affect care and contribute to stigmatization of women experiencing altered mood. Therefore, the aim of this systematic review is to provide a synthesis of educational and professional development needs and strategies for health-care providers in perinatal depression. METHODS: A systematic search of the literature was conducted in seven academic health databases using selected keywords. The search was limited to primary studies and reviews published in English between January 2006 and May/June 2015, with a focus on perinatal depression education and professional development for health-care providers. Studies were screened for inclusion by two reviewers and tie-broken by a third. Studies that met inclusion criteria were quality appraised and data extracted. Results from the studies are reported through narrative synthesis. RESULTS: Two thousand one hundred five studies were returned from the search, with 1790 remaining after duplicate removal. Ultimately, 12 studies of moderate and weak quality met inclusion criteria. The studies encompassed quantitative (n = 11) and qualitative (n = 1) designs, none of which were reviews, and addressed educational needs identified by health-care providers (n = 5) and strategies for professional development in perinatal mental health (n = 7). Consistently, providers identified a lack of formal education in perinatal mental health and the need for further professional development. Although the professional development interventions were diverse, the majority focused on promoting identification of perinatal depression and demonstrated modest effectiveness in improving various outcomes. CONCLUSIONS: This systematic review reveals a lack of strong research in multi-disciplinary, sector, site, and modal approaches to education and professional development for providers to identify and care for women at risk for, or experiencing, depression. To ensure optimal health outcomes, further research comparing diverse educational and professional development approaches is needed to identify the most effective strategies and consistently meet the needs of health-care providers. TRIAL REGISTRATION: A protocol for this systematic review was registered on PROSPERO (Protocol number: CRD42015023701 ), June 21, 2015.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.079
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0130.014
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.132
GPT teacher head0.385
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations63
Published2017
Admission routes2
Has abstractyes

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