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Responses of Chinese Elderly to the Threat of Severe Acute Respiratory Syndrome (SARS) in a Canadian Community

2007· article· en· W2046727442 on OpenAlexaffabout
Betty Shuc Han Wills, Janice M. Morse

Bibliographic record

VenuePublic Health Nursing · 2007
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineRespiratory systemSars virusIntensive care medicineSevere acute respiratory syndromeCoronavirus disease 2019 (COVID-19)Emergency medicineGerontologyPsychiatryInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe responses of Chinese elderly living in Edmonton, Canada, during the severe acute respiratory syndrome (SARS) pandemic, and their use of Western and/or traditional Chinese medicine. DESIGN: A QUAL-qual mixed method design, using grounded theory as the core method and ethnographic strategies are used to inform the cultural aspects of the study. SAMPLE: A purposeful sample of 19 Chinese elderly was interviewed and tape recorded. Four traditional Chinese Practitioners were also interviewed. METHODS: The interviews were transcribed and analyzed in Chinese and later translated into English. Data analysis utilized the constant comparison method. RESULTS: Participants experienced a 5-stage process of protecting self, family, and others, responding according to the perceived threat of SARS. Participants used both Western and traditional Chinese strategies to combat SARS. Their desire to protect others took precedence under the moral code of filial piety. Once SARS was under control, the community remained vigilant and continued to monitor for its possible reoccurrence. CONCLUSIONS: Cultural beliefs and practices within the Chinese population support the recommendations set by the health department for the protection of individuals and the community during the SARS pandemic. Therefore, the public health sector should become familiar with and support these Chinese cultural networks during pandemics.

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)0.008
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
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 integrity0.0000.001
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 head0.129
GPT teacher head0.473
Teacher spread0.344 · 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

Citations18
Published2007
Admission routes2
Has abstractyes

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