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Record W2162228749 · doi:10.1177/1049732305284010

When Family-Centered Care Is Challenged by Infectious Disease: Pediatric Health Care Delivery During the SARS Outbreaks

2005· article· en· W2162228749 on OpenAlexaff
Donna Koller, David Nicholas, R. Salter Goldie, Robin E. Gearing, Enid K. Selkirk

Bibliographic record

VenueQualitative Health Research · 2005
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsOutbreakMedicineInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)Health care deliveryHealth careFamily medicineFamily centered careSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakDiseaseNursingVirologyPathology

Abstract

fetched live from OpenAlex

In this ethnographic study, the authors examined the experiences and perspectives of children hospitalized because of SARS (severe acute respiratory syndrome), their parents, and pediatric health care providers. The sample included 5 children, 10 parents, and 8 health care providers who were directly affected by SARS during the time of the outbreaks and extreme infection control procedures. The data analyses illuminated a range of perceived experiences for this triadic sample. Issues related to social isolation due to infection control precautions were predominant. Themes included emotional upheaval, communication challenges, and changes in parental and professional roles. These findings reveal the cogent effects of SARS on family-centered care. The notion of providing family-centered care within an environment plagued by an infectious outbreak suggests an omniously difficult task. Efforts must be made to optimize family-centered care despite obstacles. The authors suggest effective clinical approaches in the event of future outbreaks.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.001

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 head0.332
GPT teacher head0.551
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.

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

Citations73
Published2005
Admission routes1
Has abstractyes

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