IMPROVING PATIENT-PROVIDER PARTNERSHIPS ACROSS THE HEALTHCARE SYSTEM
Bibliographic record
Abstract
Many older patients and their caregivers wish to be engaged in decisions around their care, but this is often not well accommodated in existing practice models. We synthesized available theories and evidence around engagement of older adults in healthcare decision-making into our previously developed “CHOICE” Patient Engagement Framework (Stolee et al., 2015; Elliott et al., 2016) and developed strategies to support meaningful partnerships of older patients and caregivers with their healthcare providers. In partnership with patients, caregivers and health care providers, this current project aimed to answer the following questions: 1) How do the CHOICE principles and strategies correspond with actual experiences of engagement? 2) What factors currently facilitate or hinder patient engagement? and 3) What resources, materials and implementation strategies are needed to support patient engagement in each health setting? We conducted observations and interviews in two healthcare settings (primary care and community care) with providers, patients, and families to understand current perspectives, practices, and facilitating/hindering factors related to patient engagement. Observation and interview data were analyzed using emergent coding as well as directed coding guided by the CHOICE framework. Using the information that emerged from the interviews and observations, resources and materials for patient/caregiver engagement have been co-created by patients, caregivers and healthcare providers, for use in multiple care settings.
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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 | 0.066 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".