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Record W4393038515 · doi:10.1177/0192513x241237609

A Qualitative Forum Analysis of Fathers’ Stressors and Support Seeking Behaviour During the COVID-19 Pandemic

2024· article· en· W4393038515 on OpenAlexafffund
Emily E. Cameron, Kaeley M. Simpson, John-Michael Bowes, Shayna Pierce, Kailey Penner, Alanna Beyak, Irlanda Gomez, Kristin Reynolds, Leslie E. Roos

Bibliographic record

VenueJournal of Family Issues · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of WinnipegChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
FundersResearch Manitoba
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Stressor2019-20 coronavirus outbreakPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Qualitative researchSociologyMedicineClinical psychologyVirologyOutbreak

Abstract

fetched live from OpenAlex

Fathers experienced high rates of mental health concerns during the COVID-19 pandemic. Social support is crucial to mitigate these problems; however, access to and quality of support were impacted by public health guidelines to increase physical distancing. Online forums offer an avenue for peer connection and support. Yet, minimal research has examined forum use during COVID-19. The objective of the current study was to examine the experiences and support needs of fathers during the pandemic through an exploratory qualitative content analysis of an online social support forum. Posts ( N = 299) and comments ( N = 2597) on Reddit’s sub-forum r/daddit (July and October 2020) were systematically analysed through a Framework Analytic Approach. Findings highlighted five main themes (with subthemes): forum use, family functioning, psychological and health factors, interpersonal functioning, and COVID-19. Findings underscore the critical need for mental health and parenting programs tailored to fathers and informing services to support father and family wellbeing.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.093
GPT teacher head0.447
Teacher spread0.354 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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