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Record W2949212587 · doi:10.1177/1049732319855961

Struggling With Reciprocity and Compassion: Mentoring Pregnant and Parenting Mothers Experiencing Vulnerability

2019· article· en· W2949212587 on OpenAlexafffund
Carla Ginn, Karen Benzies

Bibliographic record

VenueQualitative Health Research · 2019
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Calgary
FundersMax Bell Foundation
KeywordsPsychologyVulnerability (computing)Mental healthReciprocity (cultural anthropology)Developmental psychologyCompassionGrounded theoryQualitative researchSocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Transitioning from pregnancy to parenthood is particularly challenging for women living with low income and experiencing social isolation, mental illness, addiction, and/or family violence. The purpose of this qualitative study was to evaluate one component of Welcome to Parenthood, a two-generation multiple intervention program including neuroscience-based parenting education, kin and non-kin mentorship, and an engagement tool (baby kit). From late pregnancy to 2 months postpartum, mentors kept a journal regarding their experiences of mentoring mothers experiencing vulnerability. We engaged in a modified constructivist grounded theory to explore hand-written text from the journals. The core category, Struggling with Reciprocity and Compassion, influenced processes of Becoming a Mentor. Mentoring mothers experiencing vulnerability was both challenging and rewarding, requiring an inordinate amount of physical, social, emotional, and economic resources. To foster maternal mental health and infant development, pregnant and parenting women experiencing vulnerability could benefit from long-term reciprocal and compassionate mentoring.

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)0.011
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.006
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.249
GPT teacher head0.523
Teacher spread0.275 · 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 designQualitative
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

Citations4
Published2019
Admission routes2
Has abstractyes

Explore more

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