The current state of knowledge on mobile health interventions for opioid related harm: Integrating scoping review findings with the patient journey
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.
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 | 0.026 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".