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Record W2264689734 · doi:10.1177/0840470415588701

Making research integral to home care services

2015· review· en· W2264689734 on OpenAlexaffabout
Ariella Lang, Judith Shamian, Sharon Goodwin

Bibliographic record

VenueHealthcare Management Forum · 2015
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsVictorian Order of Nurses
Fundersnot available
KeywordsHealth careBusinessLeverage (statistics)NursingPublic relationsAction researchMedicinePolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

Home care is the fastest growing segment of the Canadian healthcare system, yet research on patient safety has been conducted predominantly in institutional settings. This is a case example of how Victorian Order of Nurses Canada, a national not-for-profit home and community care provider, embedded a nurse researcher to create an environment in which health services research flourished. This model strategically propelled important issues such as home care safety on to the national research and policy agendas and helped leverage change in multiple levels of the healthcare system. This is a call to action for building partnerships to have a researcher as an integral team member in organizations providing home care services.

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)0.098
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.098
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.135
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0100.012
Science and technology studies0.0030.023
Scholarly communication0.0140.020
Open science0.0030.008
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0040.002

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 head0.895
GPT teacher head0.790
Teacher spread0.106 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2015
Admission routes2
Has abstractyes

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