A framework for mobile healthcare answers to chronically illoutpatient non-adherence
Bibliographic record
Abstract
Non-adherence (also known as 'non-compliance') is a major barrier undermining healing efforts within out-of-hospital self-management programmes, resulting in waste of human and social resources. This study suggests a theoretical framework of activities through which mobile patient solutions might address non-adherence determinants in a broader context of clinical interventions. The goal of the paper is to explore a dilemma associated with such interventions: the uncertainty regarding the level of patient involvement and technology support. We follow a critical orientation approach in discussing this multi-faceted conundrum: we summarise the latest vision on adherence factors, we suggest several types of interventions through which mobile healthcare solutions could address them, and we explore in detail the dilemma of patient and technology roles. We conclude that there is no universally optimal solution, and practical conditions depending on patient, disease, treatment and healthcare system are determining factors in prescribing the level of patient involvement and technology support. Our work is intended to stimulate further research into the nature of mobile solutions in health care and, especially, into patient acceptance aspects, in an endeavour to contribute to improving adherence with minimum obtrusiveness.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.006 |
| 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".