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Record W3031164135 · doi:10.1186/s13643-020-01387-6

Effectiveness of psychotherapeutic interventions on psychological distress in women who have experienced perinatal loss: a systematic review protocol

2020· review· en· W3031164135 on OpenAlexafffundabout
Elyse M. Charrois, Katherine Bright, Abdul Wajid, Muhammad Kashif Mughal, Alix Hayden, Dawn Kingston

Bibliographic record

VenueSystematic Reviews · 2020
Typereview
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsAlberta Health ServicesAlberta Children's HospitalAlberta HealthUniversity of Calgary
FundersUniversity of Calgary
KeywordsMedicinePsychological interventionPsycINFOCINAHLMEDLINERandomized controlled trialCochrane LibrarySystematic reviewPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Perinatal loss is a traumatic and complex experience that contributes to negative maternal psychological states and adverse outcomes impacting fetal development, maternal-fetal/infant bonding, marital/partner relationships, and child cognitive, emotional, and behavioral development. These outcomes present preventable disease burden and financial liability to individuals, families, and the healthcare system. Psychological interventions have the potential to improve outcomes for women and their families after perinatal loss. A few studies have explored the effectiveness of individual psychotherapeutic interventions in reducing maternal psychological distress after perinatal loss; however, a systematic review to compare these interventions has not been conducted. The primary objective of this systematic review is to determine the effectiveness of psychotherapeutic intervention on psychological distress and perception, coping, and adjustment in women who have experienced perinatal loss. The secondary objective of this review is to examine the content and delivery methods of effective psychotherapeutic interventions. METHODS: We endeavor to search electronic databases (PsycINFO, MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, CINAHL, Social Work Abstracts, Family and Society Studies Worldwide, Family Studies Abstracts, Academic Search Premier), gray literature databases (Proquest Dissertation and Theses Global, Web of Science Conference Proceedings Citation Index, OAIster, Open-Grey, Canadian Electronic Library, Canadian Research Index), and relevant organizational websites and conduct forward and backward citation searches of included studies. Inclusion criteria will consider studies that (1) are randomized controlled trials (RCTs), quasi-experimental (e.g., before-after design), and observational (prospective cohort); (2) include women affected by perinatal loss accessing psychotherapeutic intervention or support; and (3) evaluate a mental health or related outcome. Two authors will independently screen all citations, full-text articles, and abstract data. The study methodological quality (or bias) will be appraised using an appropriate tool. The primary outcome(s) will be measurements on the severity of depressive, anxiety, grief, and post-traumatic stress symptoms. Secondary outcomes will include measurements on difficulties in perception, coping, social, or dyadic adjustment. Conducting a narrative synthesis will identify relationships within study findings, and if appropriate, a random effects meta-analysis will be performed. DISCUSSION: This systematic review will summarize the effectiveness of psychological interventions, including their content and delivery method, in reducing psychological distress and improving outcomes for women affected by perinatal loss. The evidence generated from this review can inform researchers and policymakers in expanding on related research and developing customized interventions or programs. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019126456.

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)0.059
metaresearch head score (Gemma)0.065
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.059
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.065
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0240.018
Bibliometrics0.0190.016
Science and technology studies0.0050.005
Scholarly communication0.0080.009
Open science0.0070.006
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0550.005

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 head0.137
GPT teacher head0.511
Teacher spread0.373 · 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
GenreProtocol

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

Citations19
Published2020
Admission routes3
Has abstractyes

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