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Record W4380867844 · doi:10.1037/fam0001123

“We’re treading water as best we can”: A qualitative study of parental resilience during COVID-19.

2023· article· en· W4380867844 on OpenAlexaffabout
Allison Reeves

Bibliographic record

VenueJournal of Family Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of Guelph-Humber
Fundersnot available
KeywordsStressorPsychologyPsycINFOCoping (psychology)Mental healthPsychological resilienceDevelopmental psychologyPandemicGrounded theoryFamily resilienceClinical psychologyQualitative researchSocial psychologyCoronavirus disease 2019 (COVID-19)PsychiatryMEDLINEMedicine

Abstract

fetched live from OpenAlex

This Ontario-based study utilized modified grounded theory to consider the potential burden of chronic stressors on parents of young children during the COVID-19 crisis, as well as parental experiences of coping and resilience. Cross-sectional interviews at a single point in time do not reveal change and adaptation during an evolving pandemic; for this reason, this study conducted one interview at the end of the first wave of the pandemic in Ontario and a second interview a year and a half later. Twenty parents participated in two interviews, and findings are presented using Bonanno's (2004, 2005) mental health trajectory model following life disruption. The recovery trajectory details parental stressors and challenges that returned to baseline; the chronic stress trajectory notes parental experiences of unremitting stressors; and the resilience trajectory describes helpful behaviors, beliefs, and conditions that supported parental mental wellness across both interviews. Findings reveal that the resilience and recovery trajectories were dominant among this cohort, and descriptions of both problem-based and emotional-based coping through creativity and parental innovation are presented, as well as unforeseen positive impacts of the pandemic on families. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.164
GPT teacher head0.538
Teacher spread0.375 · 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 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
Published2023
Admission routes2
Has abstractyes

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