Perceived Treatment Satisfaction and Effectiveness Facilitators Among Patients With Chronic Health Conditions: A Self-Reported Survey
Bibliographic record
Abstract
Background Approximately 50% of patients are nonadherent to prescribed medications. Patient perception regarding medication effectiveness has been linked to improved adherence. However, how patients perceive effectiveness is poorly understood. Objective The aim of this study was to elucidate factors associated with perceived treatment satisfaction and effectiveness among patients with chronic health conditions. Methods We conducted a descriptive study using a cross-sectional survey design. We administered a Web-based survey to participants with migraine, multiple sclerosis (MS), or rheumatoid arthritis (RA). Patients were recruited from established online communities of Health Union. Descriptive statistics, correlations, and comparison tests were used to examine outcomes. Results Data were collected from 1820 patients: 567 with migraine, 717 with MS, and 536 with RA. The majority of participants were female (1644/1820, 90.33%), >40 years old (1462/1820, 80.33%), and diagnosed >5 years ago (1189/1820, 65.33%). Treatment satisfaction and perceived medication effectiveness were highly correlated (r=0.90, P<.01). Overall, three temporal factors were positively correlated with satisfaction or perceived effectiveness: time on current medication (satisfaction rs=0.22, P<.01; effectiveness rs=0.25, P<.01), time since diagnosis (satisfaction rs=0.07, P<.01; effectiveness rs=0.09, P<.01), and time on treatment (effectiveness rs=0.08, P<.01). Conclusions Findings validated the strong relationship between treatment satisfaction and perceived effectiveness. Understanding the (1) positive relationship between time and treatment satisfaction and effectiveness and (2) factors associated with determining medication effectiveness can help clinicians better understand the mindset of patients regarding treatment. Clinicians may be better prepared to elicit patient beliefs, which influence medication adherence, for people diagnosed with chronic health conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".