Prioritizing the Needs of Caregivers of Older Adults to Support Their Help-Seeking Process as a First Step to Developing an eHealth Tool: The Technique for Research of Information by Animation of a Group of Experts (TRIAGE) Method
Bibliographic record
Abstract
BACKGROUND: Caregivers of functionally dependent older persons sometimes seek formal services to support their relatives. However, this process of help-seeking is complex. OBJECTIVE: The overall aim of the study was to use a co-design approach to develop an electronic health (eHealth) tool to support caregivers in their process of help-seeking. This study presents the first step of the design phase, which aimed to prioritize the user needs to be considered during the development of an eHealth tool. METHODS: A total of 3 groups of caregivers, community workers, and health and social service professionals participated in either a co-design session (1 or 2) or an advisory committee in 2 rural areas and 1 urban area. The needs identified in the academic literature and during a previous study were sorted (Technique for Research of Information by Animation of a Group of Experts [TRIAGE] method) by the participants (referred to in this study as co-designers) to obtain a consensus on those to be prioritized. Needs identified, grouped, and removed were ranked and compared. RESULTS: Of the initial list of 32 needs, 12 were modified or merged, 3 added, and 7 deleted as the co-designers felt that the needs were poorly formulated, redundant, irrelevant, or impossible to meet. In the end, 19 needs were identified for the design of the eHealth tool. CONCLUSIONS: Many of the identified needs are informational (eg, having access to up-to-date information) and are probably met by existing tools. However, many others are emotional (eg, being encouraged to use the services) and offer an interesting challenge to eHealth tool development. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11634.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".