Optimizing Patient Education of Oncology Medications: A Patient Perspective
Bibliographic record
Abstract
The medication information needs of patients with cancer have been primarily studied using quantitative methods and little qualitative research on this topic exists. The purpose of this study was to explore patients' perspectives of optimal oncology medication education provided to patients at the Nova Scotia Health Authority (NSHA). Adult (≥ 18 years) outpatients in medical, gynecological and hematology oncology at NSHA were invited to participate in focus groups, which were audio-recorded, transcribed and analyzed thematically. Three focus groups, including 21 outpatients, were conducted. Four major themes were identified: (1) preparing for what lies ahead consisted of: readiness to receive information, anxiety over the unknown, setting expectations and patients supporting one another; (2) bridging the information gaps was made up of gap in provision of patient education, gap in continuity of patient education, and gap in trustworthy information; (3) understanding the education needs of the patients was comprised of sources of information, education timing and setting, prioritizing information needs, and individuality; and (4) experience within the health care system encompassed: interactions with health care professionals, willingness to ask questions, patient satisfaction, and financial implications. This study identified previously unknown patient education needs and also supported ideas reported in the literature. This data will guide the strategies that will be used to optimize the delivery of oncology medication education at our facility and other health care institutions.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".