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Record W3157299246 · doi:10.35680/2372-0247.1538

The paradoxical injunctions of partnership in care: Patient engagement and partnership between issues and challenges

2021· article· en· W3157299246 on OpenAlexaff
Khayreddine Bouabida, Marie‐Pascale Pomey, Geneviève Cyr, Ursulla Aho-Glele, Breitner Gomes Chaves

Bibliographic record

VenuePatient Experience Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGeneral partnershipPresentation (obstetrics)Public relationsPatient experiencePoliticsHealth careQuality (philosophy)NursingKey (lock)MedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Partnership in care and patient engagement is an expanding approach and tremendously promising for improving the quality of healthcare services. However, the approach could be subject to many issues and challenges of various kinds. In this paper, we develop a reflection of the challenges and issues that the approach of patient engagement and partnership in care is facing. After a brief presentation of certain key concepts of partnership in care and patient engagement, we discuss in this paper the most worthy of consideration issues that we identified and classified as follows: Political, Financial, Organizational, Clinical, and Ethical Issues. We then conclude the paper with certain recommendations that may help to better deal with those challenges and issues and alleviate their impacts. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.

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)0.066
metaresearch head score (Gemma)0.053
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 score0.066
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0260.111
Scholarly communication0.0290.038
Open science0.0040.038
Research integrity0.0140.039
Insufficient payload (model declined to judge)0.0040.001

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 head0.407
GPT teacher head0.464
Teacher spread0.057 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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