Patient Advisors: How to implement a process for involvement at all levels of governance in a healthcare organization
Bibliographic record
Abstract
Patient involvement at the operational (clinical care and services), tactical (management), and strategic (board of directors and executive management) levels of establishments is increasingly sought after. To address this specific challenge, a Canadian healthcare organization, the Centre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-Québec, has developed an integrated strategy based on three principles: (1) shared leadership between a patient and a manager to build the strategy; (2) a clear process for recruiting, training, and coaching patient advisors (PA) so that they can participate in decision-making at the various levels of governance of the establishment; and (3) a feedback process for improving the strategy over time. This initiative gave rise to a pool of 30 patient advisors who reviewed documentation (39.07%), presented testimonies to establishment practitioners (13.73%), participated in process improvement activities (12.97%) and committees (8.93%), and helped train students in health sciences (11.61%). It also led to the development of a request form for all persons wishing to involve PAs in their projects. This PA involvement, highly appreciated by both managers (94%) and PAs (81%), brought back the fundamental meaning of the patient–practitioner relationship and helped incorporate patients’ experiential knowledge into the care and service improvement process. This strategy can serve as a model for other organizations wishing to structure optimal patient engagement at the different levels of governance of their organization.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.210 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".