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Record W4392754488 · doi:10.1177/03080226241232815

Adopting new habits and routines in response to COVID-19 lockdown disruptions: A qualitative study

2024· article· en· W4392754488 on OpenAlexafffundabout
Dorothy Kessler, Emma Boudreau, Jennifer Maitland, Rosemary Lysaght, Mary Ann McColl, Libby Alexander, Clarke Wilson, Beata Batorowicz, Vincent DePaul, Catherine Donnelly

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsProvidence Health CareQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDisconnectionFlexibility (engineering)Adaptation (eye)PandemicPsychologyCoronavirus disease 2019 (COVID-19)Qualitative researchOccupational therapyApplied psychologyMedicineSociologyPolitical scienceManagement

Abstract

fetched live from OpenAlex

Introduction: COVID-19-related restrictions resulted in changes to time use and occupational participation, impacting individual and collective well-being. This study addressed a knowledge gap concerning the adaptive process during periods of occupational disruption. We explored the experience of occupational disruption and how people managed disruption during the COVID-19 pandemic. Methods: We used a qualitative descriptive approach and interviewed 18 participants of a larger survey study of time use during the COVID-19 pandemic undertaken around a medium-sized city in Canada. Transcript analysis was conducted inductively using conventional content analysis. Findings: . In the face of disruption, participants described a sense of loss and disconnection, and challenges with time management. Establishing new habits and routines required new learning associated with increased time and flexibility, connecting with others and health and wellness. Conclusion: During changing pandemic restrictions, participants expressed a sense of loss, disconnection and time management challenges associated with occupational disruptions, but also described ways they adapted, improving their health and well-being. Strategies identified through this work may be used to enhance adaptation during disruptions. Future research should explore differences in adaptation, among more diverse populations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.216
GPT teacher head0.550
Teacher spread0.335 · 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 routes3
Has abstractyes

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