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Record W4400982383 · doi:10.1037/fam0001260

Family belief system influences on COVID-19 vaccination decisions among First Nations Australians.

2024· article· en· W4400982383 on OpenAlexaboutno aff
C. E. Blanco, Natalie Gately, Julie Ann Pooley

Bibliographic record

VenueJournal of Family Psychology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOThematic analysisGovernment (linguistics)Context (archaeology)PsychologyPandemicAnxietyIsolation (microbiology)Coronavirus disease 2019 (COVID-19)Social psychologyExploratory researchPublic relationsQualitative researchPolitical scienceSociologyMEDLINEMedicinePsychiatryLawGeographySocial scienceDisease

Abstract

fetched live from OpenAlex

COVID-19 has changed the world in many ways, and while some families were divided by geographical distances and mandatory "stay-at-home" orders during lockdowns, others became fractured owing to decisions about vaccination. This novel exploratory qualitative study questions how family systems and COVID-19 attitudes influenced the vaccine decisions of 10 Australian First Nations individuals. Despite the significance of family in decision making, the advice of respected family members became insignificant when nonvaccination resulted in the undesirable consequences of coercive government mandates. The thematic analysis identifies themes of choice, repeated wrongs of the past, trust, relationships, isolation, and parenting anxiety. It also demonstrates the resiliency of First Nations families, evident in the creative ways family systems adapted during the pandemic. This study has implications for governments and health service planning toward community COVID-19 support systems in a postpandemic context and provides ideas for further research into First Nations service provision during health crises. (PsycInfo Database Record (c) 2024 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)0.001
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 score0.558
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.162
GPT teacher head0.380
Teacher spread0.218 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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