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Record W4403828793 · doi:10.1177/08404704241293299

The power of partnership: Strategies for pan-Canadian spread and scale of paramedics providing palliative care

2024· article· en· W4403828793 on OpenAlexaffabout
Alix Carter, Cheryl Cameron, Marianne Arab, Shaw-Moxam Raquel, Andrea C. Coronado, Charlotte Pooler

Bibliographic record

VenueHealthcare Management Forum · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsCanadian Partnership Against CancerNova Scotia Cancer CentreDalhousie UniversityCARE CanadaAlberta Health ServicesCanadian Virtual UniversityCancer Care Nova ScotiaNova Scotia Health AuthorityWestern University
Fundersnot available
KeywordsBlueprintGeneral partnershipScale (ratio)Palliative careBusinessNursingHealth careMedicineMedical emergencyPolitical scienceGeographyEngineering

Abstract

fetched live from OpenAlex

Paramedics and Palliative Care is an example of a promising practice ("pilot") that underwent successful spread and scale across Canada. Through the support of two pan-Canadian health organizations and concurrent evolution of the profession of paramedicine, this innovation has become integrated into practice. Evaluation of the innovation sites showed positive impact in all elements of the Quintuple Aim, and data from the expansion sites mirrors this success. Paramedic comfort and confidence is improved. Patient and family satisfaction is high. Quality indicators such as time spent at home, and home deaths, improved after program launch. There are time and cost savings with the program in place. The framework that enabled this spread and scale is presented and elaborated, to support further uptake of this innovation and provide a blueprint for successful expansion of other promising practices to support healthcare improvement across Canada.

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)0.052
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.198
Threshold uncertainty score0.677

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0270.014
Scholarly communication0.0140.008
Open science0.0050.028
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.100
GPT teacher head0.424
Teacher spread0.324 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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