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Record W4296808076 · doi:10.1186/s13104-022-06194-x

Development of the quality of teen trauma acute care patient and parent-reported experience measure

2022· article· en· W4296808076 on OpenAlexafffund
Matthew Yeung, Brent Hagel, Niklas Bobrovitz, Thomas Stelfox, Natalie Yanchar

Bibliographic record

VenueBMC Research Notes · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryUniversity of Calgary
KeywordsMedicineMeasure (data warehouse)Quality (philosophy)Intensive care medicineData miningComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: Patient-Reported Experience Measures (PREMs) provide valuable patient feedback on quality of care and have been associated with clinical outcomes. We aimed to test the reliability of a modified adult trauma care PREM instrument delivered to adolescents admitted to hospital for traumatic injuries, and their parents. Modifications included addition of questions reflecting teen-focused constructs on education supports, social network maintenance and family accommodation. RESULTS: Forty adolescent patients and 40 parents participated. Test-retest reliability was assessed using Cohen's kappa, weighted kappa, and percent agreement between responses. Directionality of changed responses was noted. Most of the study ran during the COVID-19 pandemic. We established good reliability of questions related to in-hospital and post-discharge communication, clinical and ancillary care and family accommodation. We identified poorer reliability among constructs reflecting experiences that varied from the norm during the pandemic, which included "maintenance of social networks", "education supports", "scheduling clinical follow-ups" and "post-discharge supports". Parents, but not patients, demonstrated more directionality of change of responses by responding with more negative in-hospital and more positive post-discharge experiences over time between the test and retest periods, suggesting risk of recall bias. Situational factors due to the COVID-19 pandemic and potential risks of recall bias may have limited the reliability of some parts of the survey.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.601
GPT teacher head0.590
Teacher spread0.011 · 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 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

Citations2
Published2022
Admission routes2
Has abstractyes

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