Adopting a learning pathway approach to patient partnership in telehealth: A proof of concept
Bibliographic record
Abstract
Background: Amidst the acceleration of digital health deployment in the province of Québec, the need to clarify the role of patients and caregivers was deemed essential to guide the deployment of telehealth strategies. A patient learning pathway (PLP) approach to patient and caregiver engagement was developed, containing knowledge, abilities, and skills mobilized by patients and caregivers at key moments of the life course with an illness, as well as emerging educational needs. Objective: The objective of the current paper is to present the innovative PLP approach to patient and caregiver engagement in telehealth by applying it to three medical specialties within the context of the Québec healthcare system: dermatology, oncology, and mental health/psychiatry. Methods: The PLP methodology is constituted of five chronological phases: 1) identification and engagement of main stakeholders; 2) exploration; 3) recruitment of patient and caregiver partners; 4) co-development of PLP first draft; and 5) validation and consensus building regarding competencies. Results: Three PLPs (dermatology, oncology, and mental health/psychiatry) have already been mapped using this participatory approach, showing that the proposed PLP approach to patient and caregiver engagement in telehealth is feasible. Conclusions: Mapping patient and caregiver competencies organized throughout patients' life course with an illness can lead to a highly operationalizable tool, which relevant stakeholders can use in a way that promotes patient self-management, shared decision-making, and empowerment. Innovation: The five-step PLP methodology developed proposes an innovative and structured approach to partnership with patients and caregivers in telehealth by outlining their roles throughout their life course with an illness.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".