Reconceptualizing medication adherence: six phases of dynamic adherence
Bibliographic record
Abstract
Introduction Nonadherence is the Achilles heel of effective psychiatric treatment. The meaning of the term “adherence” has evolved over time and is now associated with a variety of definitions and measurement methods. This has resulted in a poorly operationalized and non-standardized term that is often interpreted differently by providers and patients. Objectives/Aims This abstract aims to: 1) describe changes in the concept of adherence; 2) present a more comprehensive definition of adherence which recognizes the influence of patient-provider transactions; 3) introduce dynamic adherence, a six-phase model, which incorporates the influence of transactional processes and econometrics on patients’ adherence decisions; and 4) provide recommendations for providers to improve their relationships with patients and in turn, medication adherence. Methods A review of the scientific mental health literature. Results Despite the prevalence, seriousness, and costs associated with medication nonadherence, the construct of adherence remains poorly operationalized and lacks cogent standardization. Drawing from psychiatric research, a dynamic model of medication adherence across six phases is presented. Conclusions This model of adherence highlights the importance of the patient-provider relationship and the transactional processes that comprise what is a dynamic developmental system. Dynamic adherence is intended to foster movement toward a more coherent and unified set of definitions and clinical strategies that will provide the potential to more fully elucidate the risk and protective mechanisms impacting adherence, and the subsequent development and refinement of best practices in increasing the odds of stable medication adherence.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".