Development of a Patient Education Newsletter in an Oncology Pharmacy Practice
Bibliographic record
Abstract
INTRODUCTION Little has been written about the role of patient education newsletters in health care and even less about their use in pharmacy practice. A patient education newsletter has the potential to fulfill several roles. It can allow direct communication with patients, family members, and others in the community.1 It is an efficient means of communication, in that patients often have similar questions and concerns.1,2 A newsletter may also facilitate communication by stimulating patients to discuss issues that they might not otherwise mention.1 Lastly, patient newsletters are a much-needed source of accurate and easy-to-understand health information in the community.1 In response to the results of a patient survey,3 the Cross Cancer Institute Pharmacy Department of the Alberta Cancer Board developed a patient education newsletter. The survey, which was circulated at each of the 15 pharmacies of the Alberta Cancer Board (2 tertiary sites in Edmonton and Calgary and 13 community cancer centres), indicated which services were desired by patients of the Pharmacy Department. Seventy-eight percent of the 114 respondents felt that a newsletter would be useful to them; this was the highest-rated of 13 services proposed in the survey. The objective of this project was to develop 12 monthly newsletters on pharmacy-related topics of interest to cancer patients.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".