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Record W4324378449 · doi:10.3389/fgwh.2023.1027409

Examining the gaps in perinatal mental health care: A qualitative study of the perceptions of perinatal service providers in Canada

2023· article· en· W4324378449 on OpenAlexaffabout
Christina DeRoche, Amanda Hooykaas, Christine Ou, Jaime Charlebois, Krista King

Bibliographic record

VenueFrontiers in Global Women s Health · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMemorial University of NewfoundlandInstitute of AgingUniversity of VictoriaUniversity of GuelphCanadore College
Fundersnot available
KeywordsMental healthService providerPerceptionNursingQualitative researchMental health careHealth careMedicineService (business)PsychologyPsychiatryBusinessPolitical scienceSociology

Abstract

fetched live from OpenAlex

In Canada, access to perinatal mental health services is disparate across districts, regions, provinces, and territories. Questions remain as to how gaps in service are being experienced by Canadian service providers and clinicians. This paper examines three key questions: 1) What are the experiences of care providers with respect to the screening, identifying, and managing perinatal mental health disorders? 2) What gaps in perinatal mental health care have been identified? and 3) What approaches have been taken by providers, communities, and regions in addressing the needs of their populations? To address these questions, 435 participants from across Canada were surveyed using an online survey constructed by the research members of the CPMHC. A qualitative analysis of the data revealed three key themes: groups marginalized by the current perinatal mental health system, gaps and supports identified by communities; and systemic and policy issues. From these three themes we have identified the key components of changes required in the national approach to perinatal mental health disorders. We identify key resources that could be utilized to create policy change and provide recommendations for change.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.330
Teacher spread0.313 · 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 designObservational
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

Citations28
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Global Women s HealthSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207