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Record W4381158165 · doi:10.1002/jcop.23070

Resilience practices among a broad spectrum of individuals with physical disabilities during the COVID‐19 pandemic: A qualitative photo elicitation study

2023· article· en· W4381158165 on OpenAlexaff
Gurkaran Singh, Alfiya Battalova, William C. Miller, Ethan Simpson, Isabelle Rash, Somayyeh Mohammadi, Gordon Tao, Janice Chan, W. Ben Mortenson

Bibliographic record

VenueJournal of Community Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsRoyal Roads UniversityInternational Collaboration On Repair DiscoveriesGF Strong Rehabilitation CentreUniversity of British Columbia
Fundersnot available
KeywordsCognitive reframingPsychologyPandemicPsychological resilienceResilience (materials science)Qualitative researchCommunity resilienceRecreationPhoto elicitationSocial supportCoronavirus disease 2019 (COVID-19)Social psychologySociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

This community-based study explored resilience practices among people living with physical disabilities (i.e., stroke, spinal cord injury, and other physical disabilities) during the COVID-19 pandemic. In this photo elicitation study, during 1:1 interviews, participants shared and described photos that reflected their pandemic-related experiences. Data were analyzed thematically to identify resilience-related practices. Our analysis revealed three themes: (1) reflecting on the importance of family, friends, and community (e.g., recalling past memories and strengthening existing connections); (2) engaging in social and recreational activities (e.g., experiencing the outdoors and gardening); and (3) reframing personal contexts and social environment (e.g., adjusting to new social norms and overcoming physical barriers to navigating safely during the pandemic). The resilience that participants identified encompassed not only individual strategies but also family and community supports. Resilience can be fostered through community initiatives that support more equitable responses to health emergencies for people with disabilities.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.053
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.135
GPT teacher head0.480
Teacher spread0.344 · 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
Published2023
Admission routes1
Has abstractyes

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