Understanding a Patient’s Approach to Medication Use: An Aid to Tailoring Medication Information
Bibliographic record
Abstract
ABSTRACT On average, 50% of patients do not take their medications as prescribed and therefore may not receive maximum therapeutic benefit. This problem may be intentional or unintentional. Intentional nonadherence, stemming from the patient’s decision to self-regulate his or her medications without input from or knowledge of the physician, occurs frequently in spite of clear information about medications and instructions for their use delivered by physicians and pharmacists. In particular, patients receiving long-term medications regularly make conscious decisions to adjust their drug regimens. It has been proposed that a more patient-centred approach would address both intentional and unintentional nonadherence by incorporating information about an individual patient’s decision-making into the medicine education process, thereby allowing the pharmacist to tailor education to the needs and context of the individual patient. This article examines some of the psychological factors underlying medication self-regulation by patients. It also explores strategies that pharmacists and other health care providers can use to examine intentional medication nonadherence and to support the development of effective medication-taking practices by tailoring their medication conversations with patients. ABSTRACT En moyenne, 50 % des patients ne prennent pas leurs medicaments comme prescrits et, par consequent, n’en tirent peut-etre pas les bienfaits therapeutiques maximums. Ce probleme de non-observance therapeutique peut etre intentionnel ou non. La non-observance intentionnelle, attribuable a la decision du patient d’autogerer la prise de ses medicaments sans en informer son medecin ou lui demander son avis, est courante, malgre les renseignements clairs que donnent les medecins et les pharmaciens sur les medicaments et la facon de les prendre. Plus particulierement, il arrive souvent que les patients qui prennent des medicaments a long terme decident en toute connaissance de cause de modifier leur traitement. On a emis l’hypothese selon laquelle une approche davantage centree sur le patient permettrait de s’attaquer au probleme de non-observance intentionnelle ou non, si on y integrait des renseignements sur la prise de decision par un patient particulier dans la demarche de l’enseignement sur les medicaments, permettant ainsi au pharmacien d’adapter l’information aux besoins et au contexte de chaque patient. Cet article se penche sur certains des facteurs psychologiques qui sous-tendent l’autogestion de la prise des medicaments par les patients. Il examine egalement des strategies que les pharmaciens et d’autres professionnels de la sante peuvent utiliser pour comprendre la non-observance therapeutique intentionnelle et les aider a favoriser de bonnes habitudes de prise des medicaments, en individualisant l’information sur les medicaments qu’ils transmettent aux patients.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".