Involving Citizen-Patients in the Development of Telehealth Services: Qualitative Study of Experts’ and Citizen-Patients’ Perspectives
Bibliographic record
Abstract
BACKGROUND: Decisions regarding telehealth services in Quebec (Canada) have been largely technocratic by nature for the last 15 years, and the involvement of citizen-patients in the development of telehealth services is virtually nonexistent. In view of the societal challenges that telehealth raises, citizen-patient involvement could ensure more balance between evidence from traditional research methodologies and technical experts and the needs and expectations of populations in decisions about telehealth services. OBJECTIVE: This study aimed to explore the perception of various stakeholders (decision makers, telehealth program and policy managers, clinicians, researchers, evaluators, and citizen-patients) regarding the involvement of citizen-patients in the development of telehealth services in Quebec. In particular, we explored its potential advantages, added value, obstacles, and challenges it raises for decision making. METHODS: We used a qualitative research approach based on semistructured individual interviews, with a total of 29 key actors. Respondents were identified by the contact network method. Interviews were recorded and transcribed verbatim. A pragmatic content thematic analysis was performed. To increase the capacity for interpretation and analysis, we were guided by the principle of data triangulation. RESULTS: Citizen-patient involvement in decision making is perceived more as a theoretical idea than as a practical reality in health care organizations or in the health system. There is very little connection between citizen involvement structures or patient and user groups and telehealth leaders. For the respondents, citizen-patient involvement in telehealth could increase the accountability and transparency of decision making and make it more pragmatic within an innovation-driven health system. This involvement could also make citizen-patients ambassadors and promoters of telehealth and improve the quality and organization of health services while ensuring they are more socially relevant. Challenges and constraints that were reported include the ambiguity of the citizen-patient, who should be involved and how, claimant citizen-patient, the risk of professionalization of citizen-patient involvement, and the gap between decision time versus time to involve the citizen-patient. CONCLUSIONS: This study provides a basis for future research on the potential of involving citizen-patients in telehealth. There is a great need for research on the issue of citizen-patient involvement as an organizational innovation (in terms of decision-making model). Research on the organizational predisposition and preparation for such a change becomes central. More efforts to synthesize and translate knowledge on public participation in decision making in the health sector, particularly in the field of technology development, are needed.
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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 | 0.025 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".