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Record W2131532140 · doi:10.1017/s0714980809090163

Community-Based Home Support Agencies: Comparing the Quality of Care of Cooperative and Non-profit Organizations

2009· article· fr· W2131532140 on OpenAlexaffabout
Catherine Leviten‐Reid, Ann Hoyt

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2009
Typearticle
Languagefr
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

RÉSUMÉ Au Québec, une combinaison d’organismes sans but lucratif et de coopératives offre des services d’entretien ménager, de préparation de repas et d’aide aux courses aux personnes âgées. Dans la présente étude, on pose la question suivante : les services offerts par les coopératives de services à domicile sont-ils de meilleure qualité que les services offerts par les organismes sans but lucratif? Cette étude permet également d’examiner les répercussions déterminées de la participation des bénéficiaires et des travailleurs et travailleuses au conseil d’administration. Les données ont été recueillies en 2006 et 2007 auprès de 831 personnes bénéficiaires de services à domicile, assurés par 9 coopératives et 9 organismes sans but lucratif. Deux instruments de mesure de la qualité centrés sur les bénéficiaires ont été utilisés : une échelle d’évaluation sommative de la qualité en 39 points et une note globale de qualité en 4 points. Les données ont été analysées par régression logistique. Les résultats révèlent que la structure organisationnelle n’est pas une variable explicative de la qualité, mais que la participation des travailleurs et des travailleuses au conseil d’administration est associée positivement à la note de satisfaction. De plus, la participation des bénéficiaires est associée positivement à la note de qualité globale.

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)0.007
metaresearch head score (Gemma)0.028
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 score0.621
Threshold uncertainty score0.762

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.058
GPT teacher head0.346
Teacher spread0.288 · 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

Citations7
Published2009
Admission routes2
Has abstractyes

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