Measuring the Influence of Side Effect Expectations, Beliefs, and Incident Side Effects on the Risk for Drug Discontinuation Among Individuals Starting New Medications, a Cross-sectional Study
Bibliographic record
Abstract
Purpose: To measure the impact of beliefs, expectations, side effects, and their combined effects on the risk for medication nonpersistence. Patients and methods: Using a cross-sectional design, individuals from Saskatchewan, Canada who started a new antihypertensive, cholesterol-lowering, or antihyperglycemic medication were surveyed about risk factors for nonpersistence including: (a) beliefs measured by a composite score of three questions asking about the threat of the condition, importance of the drug, and harm of the drug; (b) incident side effects attributed to treatment; and (c) expectations for side effects before starting treatment. Descriptive statistics and logistic regression models were used to quantify the influence of these risk factors on the outcome of nonpersistence. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Results: Among 3,029 respondents, 5.8% (n=177) reported nonpersistence within four months after starting the new drug. After adjustment for numerous covariates representing sociodemographics, health-care providers, medication experiences and beliefs, both negative beliefs (OR: 7.26, 95%CI: 4.98-10.59) and incident side effects (OR: 8.00, 95%CI: 5.49-11.68) were associated with the highest odds of nonpersistence with no evidence of interaction. In contrast, expectations for side effects before starting treatment exhibited an important interaction with incident side effects following treatment initiation. Among respondents with incident side effects (n=741, 24.5%), the risk for early nonpersistence was 11.5% if they indicated an expectation for side effects before starting the medication compared to 23.6% if they did not (adjusted OR: 0.38, 95%CI: 0.25-0.60). Conclusion: Expectations for side effects may be a previously unrecognized but important marker of the probability to persist with treatment. A high percentage of new medication users appeared unprepared for the possibility of side effects from their new medication making them less resilient if side effects occur.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".