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Record W7008597493

Community coherence during COVID-19 – a pilot study

2023· article· en· W7008597493 on OpenAlexaboutno aff

Bibliographic record

VenueSocio-Environmental Systems Modeling · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessFocus groupQualitative researchCoherence (philosophical gambling strategy)Cohesion (chemistry)Public healthCitizen journalismCommunity-based participatory research
DOInot available

Abstract

fetched live from OpenAlex

Using a salutogenic perspective, this qualitative pilot study aimed to explore the experiences of university staff and students in various Western countries with community coherence during COVID-19. This entailed a focus on individual and community assets that contributed to positive experiences with community coherence during the pandemic. Sixteen participants from University staff and postgraduate students in Europe and Canada interested in Public Health were included. The study was conducted online via Microsoft Teams using the Structured Interview Matrix method. This participatory facilitating method enabled participants to dialogue about their experiences with community coherence during COVID-19. The results show that during COVID-19, participants primarily engaged in activities related to personal health and well-being, related to close family, friends and neighbours and an increased need to use digital technologies in their free time and during working hours. Key themes observed across the various international communities during the times when high levels of restrictions were in place, were a greater sense of loneliness and vulnerability. This pilot study indicates that there was a high level of community cohesion during COVID-19 and that people, despite living in different countries, were active in very similar ways.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.002

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 head0.203
GPT teacher head0.446
Teacher spread0.243 · 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.

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 routes1
Has abstractyes

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