MétaCan
Menu
Back to cohort
Record W4382402542 · doi:10.3390/disabilities3030020

Living through the Pandemic with a Disability: A Longitudinal Qualitative Study

2023· article· en· W4382402542 on OpenAlexafffund
Janice Chan, Somayyeh Mohammadi, Elham Esfandiari, Julia Schmidt, W. Ben Mortenson, William C. Miller

Bibliographic record

VenueDisabilities · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsInternational Collaboration On Repair DiscoveriesGF Strong Rehabilitation CentreUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsPandemicQualitative researchCoronavirus disease 2019 (COVID-19)PsychologyGovernment (linguistics)Face (sociological concept)Qualitative propertySociologyMedicineSocial scienceComputer scienceDisease

Abstract

fetched live from OpenAlex

This study investigated the experiences of people with disabilities during the first year of the COVID-19 pandemic. Four semi-structured qualitative interviews were conducted individually with 13 participants between May 2020 and February 2021. The data were thematically analyzed. Three themes were identified: (1) “Being an active agent in changing how things are done in the face of COVID restrictions”, revealed changes that participants made to their daily routines resulting from government-imposed and self-imposed restrictions; (2) “Pandemic restrictions wreak havoc”, explained participants challenges with adapting to the restrictions; and (3) “Trying to be resilient in the face of pandemic changes” described participants’ efforts to cope with life during the pandemic. The findings illustrate how life changed for people with disabilities during the pandemic. Participants reported specific types of challenges at each time point. As the vaccine rollout became more imminent, participants expressed more hope for the future and getting back to normal.

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 score0.139
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.222
GPT teacher head0.506
Teacher spread0.285 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueDisabilitiesSame topicCOVID-19 and Mental HealthFrench-language works237,207