The Impact of a Multimedia Informational Intervention on Healthcare Service Use Among Women and Men Newly Diagnosed With Cancer
Bibliographic record
Abstract
This quasi-experimental longitudinal study documented the impact of a comprehensive cancer informational intervention using information technology on healthcare service use among individuals newly diagnosed with cancer. Women with breast cancer (n = 205) and men with prostate cancer (n = 45) were recruited within 8 weeks of diagnosis at 4 university teaching hospitals in Montreal, Quebec, Canada. The intervention group (n = 148) received a 1-hour training on information technology use, a CD-ROM on cancer, and a list of reputable cancer-related Web sites. The intervention material was available for a period of 8 weeks. The control group (n = 102) received usual care. Self-reported questionnaires were completed at T1 (baseline), T2 (1 week after intervention), and T3 (3 months after intervention). Using multivariate statistics, the experimental group reported significantly more satisfaction with cancer information received compared to the control group. No significant differences were found between experimental and control groups in their reliance on healthcare services. However, women as opposed to men spent more time with nurses, were more satisfied with cancer information received, and relied more heavily on health services. Future research would explore whether the latter observations reflect genuine sex differences or are more contingent on the specific cancer diagnosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".