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Record W4295358297 · doi:10.12927/hcpol.2022.26903

Public Health Messaging during the COVID-19 Pandemic and Its Impact on Family Caregivers’ COVID-19 Knowledge

2022· article· en· W4295358297 on OpenAlexaffvenueabout
Deirdre McCaughey, Gwen McGhan, Kristin Flemons, Whitney Hindmarch, Kim Brundrit

Bibliographic record

VenueHealthcare policy · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsAlzheimer Society of CanadaUniversity of Calgary
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Government (linguistics)Public health2019-20 coronavirus outbreakDementiaSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health carePublic relationsMedicineBusinessNursingPolitical scienceVirology

Abstract

fetched live from OpenAlex

BACKGROUND: Enabling accurate, accessible public health messaging is a critical role of public health officials during a pandemic, but family caregivers of people living with dementia (PLWD) have rarely been specifically addressed in public health messaging. OBJECTIVE: The objective of this study was to examine how family caregivers for people living with dementia access and evaluate public health messaging in Alberta. METHOD: An online survey was conducted with family caregivers for PLWD (n = 217). RESULTS: Most respondents rated public health messaging as good or excellent (63.9%), but specific information about how to access caregiving information (69.5%) and what to expect in the future (49.1%) was rated as less than good. Family caregivers also identified how to care for a PLWD during the pandemic (57.5%) as a key information need. Healthcare providers/workers were the least frequently used source of public health messaging. Almost all family caregivers (94.4%) rated their own COVID-19 knowledge as good or excellent. DISCUSSION: Tailored, context-driven public health messaging for family caregivers of PLWD is critically needed.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0140.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.003
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 head0.275
GPT teacher head0.540
Teacher spread0.265 · 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 designNot applicable
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

Citations5
Published2022
Admission routes3
Has abstractyes

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