MétaCan
Menu
Back to cohort
Record W2794342229 · doi:10.1177/0840470417747003

Patient and family engagement in Alberta Health Services: Improving care delivery and research outcomes

2018· article· en· W2794342229 on OpenAlexaffabout
Sarah Singh, Katharina Kovacs Burns, Jennifer Rees, Deanna Picklyk, Jessica Spence, Nancy Marlett

Bibliographic record

VenueHealthcare Management Forum · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsHealth care deliveryHealthcare deliveryHealth services researchHealth careNursingHealth servicesOutcomes researchBusinessFamily medicineMedicineEnvironmental healthAlternative medicinePublic healthPolitical science

Abstract

fetched live from OpenAlex

Engaging patients and families in research and the design of quality improvement is an essential component of Patient and Family Centred Care (PFCC). Alberta Health Services (AHS) has been engaging patients and families to promote a cultural shift towards PFCC. The AHS trains patient and family advisors to share their experiences and encourages staff to work with advisors to co-design improvements in care. This article briefly describes the role and growth of patient and family advisors, advisory groups, and the participation of advisors in research initiatives through AHS’ Strategic Clinical Networks TM . It also describes recent efforts to build AHS’ patient and family engagement capacity by introducing standard patient engagement training, supporting the creation of the innovative Patient and Community Engagement Research internship program, and by developing tools to measure the impact of patient and advisors on AHS. And finally, this article provides key learnings for health leaders.

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)0.023
metaresearch head score (Gemma)0.041
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: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.154
GPT teacher head0.442
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 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

Citations20
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare Management ForumSame topicMental Health and Patient InvolvementFrench-language works237,207