Adopting a learning pathway approach to patient partnership in telehealth: A proof of concept
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it