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Record W2777203573 · doi:10.1177/1049732317748314

Maternal Knowing and Social Networks: Understanding First-Time Mothers’ Search for Information and Support Through Online and Offline Social Networks

2017· article· en· W2777203573 on OpenAlexafffundabout
Sheri Price, Megan Aston, Joelle Monaghan, Meaghan Sim, Gail Tomblin Murphy, Josephine Etowa, Michelle Pickles, Andrea Hunter, Victoria Little

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsConcordia UniversityNova Scotia Health AuthorityUniversity of OttawaDalhousie University
FundersCanadian Institutes of Health Research
KeywordsPostpartum periodOnline and offlineSocial supportPeriod (music)PsychologyPeer supportSocial mediaFocus groupDevelopmental psychologySocial psychologyApplied psychologySociologyComputer sciencePregnancyWorld Wide Web

Abstract

fetched live from OpenAlex

The postpartum period is an exciting yet stressful time for first-time mothers, and although the experience may vary, all mothers need support during this crucial period. In Canada, there has been a shift for universal postpartum services to be offered predominantly online. However, due to a paucity of literature, it is difficult to determine the degree to which mothers' needs are being effectively addressed. The aim of this study was to examine and understand how first-time mothers accessed support and information (online and offline) during the first 6 months of their postpartum period. Using feminist poststructuralism methodology, data were collected from focus groups and e-interviews, and analyzed using discourse analysis. Findings indicate that peer support is greatly valued, and mothers often use social media to make in-person social connections. Findings highlight how accessing support and information is socially and institutionally constructed and provide direction for health professionals to provide accessible postpartum care.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.387
GPT teacher head0.539
Teacher spread0.152 · 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.

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

Citations86
Published2017
Admission routes3
Has abstractyes

Explore more

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