Medication Management Frameworks in the Context of Self-Management: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Many individuals take multiple prescribed and unprescribed medications, also known as polypharmacy, which can be problematic. Improving medication self-management is important; however, most medication management frameworks focus on adherence and limit the integration of the core components of self-management. Therefore, the objective of this scoping review was to identify what is reported in the literature on medication management frameworks or models within the context of self-management. METHODS: Electronic databases (Medline, Embase, CINAHL and Cochrane Library) and grey literature (healthcare and government organization websites) were searched for articles that described a framework or model developed or adapted for medication management, included components of self-management and was published from January 2000 to January 2020. During the screening of titles and abstracts, 5668 articles were reviewed, 5242 were excluded and 426 were then assessed at the full-text level. Thirty-nine articles met the eligibility criteria and were included in the review. RESULTS: About half of the frameworks were newly developed (n=20), while the other half were adapted from, or applied, a previous model or framework (n=19). The majority of frameworks focused on medication adherence and most of the self-management domains were categorized as medical management, followed by emotional and role management. CONCLUSION: Medication self-management is a complex process and often impacts multiple areas of an individual's life. It is important for future frameworks to incorporate a comprehensive, holistic conceptualization of self-management that is inclusive of the three self-management domains - medical, emotional and role management.
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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".