The current state of knowledge on mobile health interventions for opioid related harm: Integrating scoping review findings with the patient journey
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
Opioid-related harm has become a major public health crisis around the world. There is a paucity of literature that examines the state of mHealth technologies in relation to the prevention and management of opioid-related harm. The purpose of this research is to examine the current state of knowledge with respect to mHealth technologies focused on opioid harm reduction and to identify gaps and technological opportunities. This research was conducted in two phases. The first phase involved the completion of a scoping review in six peer-reviewed research databases and grey literature searches in two search engines. The second phase involved the development of a Patient Journey Map to describe the findings of the scoping review in order to identify mHealth gaps and opportunities in relation to the recovery-oriented cascade of care. For the scoping review, nine articles met the inclusion criteria. These articles focused on accessibility, utilization, acceptability, feasibility and patient outcomes of mHealth interventions. These studies showed mHealth interventions are highly accessible, utilized and acceptable to opioid users, feasible to implement and can improve appointment adherence and patient outcomes. The Patient Journey Map demonstrates future mHealth interventions should focus on the prevention, diagnosis and post-recovery phases of the patient journey.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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