End user and implementer experiences of mHealth technologies for noncommunicable chronic disease management in young adults: a qualitative systematic review protocol
Bibliographic record
Abstract
Review objective: The objective of this review is to systematically identify, review and synthesize relevant qualitative research on end user and implementer experiences of mobile health (mHealth) technologies developed for noncommunicable chronic disease management in young adults. “End users” are defined as young people aged 15–24 years, and “implementers” are defined as health service providers, clinicians, policy makers and administrators. The two key questions we wish to systematically explore from identified relevant qualitative studies or studies with qualitative components are: What are users’ (end user and implementer) experiences with mHealth technologies to support health interventions for the management of chronic noncommunicable conditions, including persistent musculoskeletal pain? What factors do users (end user and implementer) perceive or experience as facilitators or barriers to the uptake and/or implementation of mHealth technologies for young people with chronic noncommunicable conditions, including persistent musculoskeletal pain?
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.
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 | 0.135 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".