A mixed method study design to explore the adherence of haematological cancer patients to oral anticancer medication in a multilingual and multicultural outpatient setting: The MADESIO protocol
Bibliographic record
Abstract
BACKGROUND: Patients with haematologic malignancies are increasingly treated by oral anticancer medications, heightening the challenge of ensuring optimal adherence to treatment. However, except for chronic myelogenous leukaemia or acute lymphoid leukaemia, the extent of non-adherence has rarely been investigated in outpatient settings, particularly for migrant population. With growing numbers of migrants in Belgium, identifying potential differences in drug use is essential. Also, previous research regarding social determinants of health highlight important disparities for migrant population. Difficulties in communication between health caregivers and patients from different cultural and ethnic backgrounds has been underlined. METHODS: Using a sequential mixed method design, the MADESIO protocol explores the adherence to oral anticancer medications in patients with haematological malignancies and among first and second generation migrants of varied origin. Conducted in the ambulatory setting, a first quantitative strand will measure adherence rates and associated risk factors in two sub-groups of patients with haematological malignancies (group A: first and second generation migrants and group B: non-migrants). The second qualitative strand of this study uses semi-structured interviews to address address the patients' subjective meanings and understand the statistical associations observed in the quantitative study (strand one). MADESIO aims to provide a first assessment of whether and why migrants constitute a population at risk concerning adherence to oral anticancer medications. DISCUSSION: Our protocol is designed to provide a comprehensive understanding of adherence in a specific population. The methodological choices applied allow to explore adherence among patients from diverse linguistic and cultural backgrounds. A particular emphasis has been paid to minimize the biases and increase the reliability of the data collected. Easily reproductible, the MADESIO design may help healthcare services to screen adherence to Oral anticancer medications and to guide providers in choosing the best strategies to address medication adherence of migrants or minority diverse population.
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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 | 0.047 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".