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Record W3011769909 · doi:10.11575/prism/32610

An Examination of Best Practice in Multi-Service Senior Centres

2013· article· en· W3011769909 on OpenAlexaboutno aff
L.D. MacRae-Krisa, Jennifer Paetsch

Bibliographic record

VenuePRISM (University of Calgary) · 2013
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersJohn William Pope FoundationNorthShore University HealthSystemNuclear Safety and Security CommissionCrown Family PhilanthropiesAstellas USA FoundationProMedicaWells FargoAstellas PharmaChicago Community Trust
KeywordsService (business)BusinessMarketing

Abstract

fetched live from OpenAlex

Since 1976, the Kerby Centre has provided a one-stop-shop for educational, social, wellness, and recreational services and supports for Calgary’s seniors, with the vision of “a happy, healthy senior population.” With plans to relocate its programs and services to a new facility to better serve older Calgarians, the Kerby Centre sought information regarding best practice models in multi-service senior centres. The Kerby Centre contracted the Canadian Research Institute for Law and the Family to conduct a best practice literature review and environmental scan of best practice models for multipurpose senior centres. It is expected that this report will aid the Kerby Centre in future planning with regard to the new facility. The purpose of this project was to examine emerging trends and best practices (e.g., commonly implemented and/or innovative practices) for multi-purpose senior centres in other jurisdictions. Specifically, this project had the following objectives: (1) To determine key facility/amenity components for an ideal multi-purpose senior centre; (2) To determine key programs for an ideal multi-purpose senior centre; (3) Recommend strategic partnerships that could better position senior centres for success; and (4) Develop five to seven profiles of leading-edge multi-purpose senior centres as recommended targets for further investigation.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.312
Teacher spread0.286 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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