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Record W2019637276 · doi:10.1108/13660750110391520

Measures of quality in long‐term care facilities

2001· article· en· W2019637276 on OpenAlexaffabout
Kevin J. Leonard, Doreen Wilson, Olga W. Malott

Bibliographic record

VenueLeadership in Health Services · 2001
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsQuality (philosophy)Health carePatient satisfactionTerm (time)Health care qualityNursingChristian ministryMarketingSet (abstract data type)Long-term careBusinessMedicineComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Although marketing does not play a large role in the Canadian health‐care system, acute care facilities have been conducting patient satisfaction surveys as a quality measurement tool for a number of years. More recently those in the long‐term care system have expressed an interest in this concept. This study set out to determine if long‐term care facilities in the Ministry of Health, Ontario Central West Region, conduct consumer satisfaction surveys. If they do, the study asked how the information is utilized and, if they do not, why not. This paper will highlight issues of service quality, health‐care quality and health‐care consumer satisfaction. This study is focused on long‐term care; however, the majority of the available research and information pertains to the acute care system. Although the principles of quality measurement and consumer satisfaction are the same for acute and long‐term care, our findings will identify the unique ways in which these principles apply to the long‐term care system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.449
GPT teacher head0.486
Teacher spread0.037 · 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 source (direct Gemma or distilled Codex), 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
Published2001
Admission routes2
Has abstractyes

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