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Record W4413059998 · doi:10.1097/or9.0000000000000180

“We can do better”: preferred practices and areas for improvement while working with patient advisors in professional organization committees

2025· article· en· W4413059998 on OpenAlexaffabout
Sevtap Savas, Nadine Frisk, Tristan Bilash, Chantale Thurston, Kimberley Thibodeau

Bibliographic record

VenueJournal of Psychosocial Oncology Research and Practice · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcGill University Health CentreCanadian Association of Psychosocial OncologyMemorial University of Newfoundland
Fundersnot available
KeywordsRemunerationPsychosocialBest practiceEquity (law)Public relationsCompensation (psychology)Work (physics)BusinessProfessional associationFinancial compensationDiversity (politics)Medical educationNursingMedicinePsychologyFinanceManagementPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Abstract Background: As the best practices for working with patient advisors in organizational committees are still under development, we sought to identify our own experience as the Canadian Association of Psychosocial Oncology—Advocacy Committee. Methods: Committee communications including meeting minutes, email correspondences, and transcript of a webinar delivered by the authors were reviewed to identify the key patient advisor experiences and preferences. Results: We identified practices in good standing and areas to improve. The main areas to improve were related to circumstances or preferences of patient advisors; financial compensation; access and health issues; and the nature of partnerships. The preferred and recommended practices included providing safe spaces; empowering, respecting, and valuing patient advisors; providing resources, guidelines, and remuneration to patient advisors; increasing advisor diversity through more extensive recruitment; and removing the barriers. Conclusions: There is a need for improving patient advisors' experiences for equity and optimum committee work.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.338
GPT teacher head0.556
Teacher spread0.218 · 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 designNot applicable
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

Citations0
Published2025
Admission routes2
Has abstractyes

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