Predictors of treatment adherence in patients with chronic disease using the Multidimensional Adherence Model: unique considerations for patients with haemophilia
Bibliographic record
Abstract
Abstract Introduction Adherence to treatment recommendations in patients with chronic disease is complex and is influenced by numerous factors. Haemophilia is a chronic disease with reported levels of adherence ranging from 17–82%. Aim Based on the theoretical foundation of the World Health Organization Multidimensional Adherence Model, the objective of this study was to identify the best combination of the variables infusion frequency, annualised bleed rate, age, distance to haemophilia treatment centre (HTC) and Haemophilia Joint Health Score (HJHS), to predict adherence to treatment recommendations in patients with haemophilia A and B on home infusion prophylaxis in Canada. Methods A one-year retrospective cohort study investigated adherence to treatment recommendations using two measures: 1) subjective report via home infusion diaries, and 2) objective report of inventory ordered from Canadian Blood Services. Stepwise regression was performed for both measures. Results Eighty-seven patients with haemophilia A and B, median age 21 years, were included. Adherence for both measures was 81% and 93% respectively. The sample consisted largely of patients performing an infusion frequency of every other day (34%). Median scores on the HJHS was 10.5; annualised bleed rate was two. Distance to the HTC was 51km. Analysis of the objective measure weakly supported greater infusion frequency as a treatment-related factor for the prediction of lower adherence, however the strength of this relationship was not clinically relevant (R2=0.048). For the subjective measure, none of the explanatory variables were significant. Conclusion Adherence is a multifaceted construct. Despite the use of theory, most of the variance in adherence to treatment recommendations in this sample of patients with haemophilia remains unknown. Further research on other potential predictors of adherence, and possible variables and relationships within factors of the MAM is required.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".