Who are the users of publicly reported cancer treatment wait times (WT)?
Bibliographic record
Abstract
6043 Background: Greater participation by patients in health care decision-making and public concerns about WT led Cancer Care Ontario (CCO) to post radiation WT by cancer type and treatment centre on its website http://www.cancercare.on.ca anticipating patients and referring physicians (MDs) would use WT information to access facilities with shorter waits. The availability of more radiation treatment facilities in Southern Ontario provides patients and MDs with more options for location of treatment. Methods: The usefulness of this approach was evaluated by an on-line survey, patient focus groups, physician interviews and usability lab testing. Results: 1,043 on-line surveys were completed by patients/family members (35%), media (24%) and others, including health professionals (HP) and administrators (AD). HP found the information useful and clear but patients were less satisfied, wanting information on the effect of WT on their illness. 45 individuals (40 treated patients, 5 family) participated in 8 geographically dispersed focus groups. Facilitator-led conversations were recorded, transcribed verbatim and content grouped in themes by 4 researchers. Most patients were unaware of the CCO WT information. Patients indicated that MDs should have and use WT information and determine speed of access to care based on urgency of condition. Patients would accept MD advice to travel to a more distant treatment facility but patients questioned why WT existed and why health care system not managed more efficiently. 15 MD phone interviews indicated MDs were distrustful of WT data, did not use the web data for referral, preferring usual practice patterns. MDs were reluctant to share WT information with patients for fear of creating unnecessary anxiety. AD found data useful as a stimulus for performance improvement. Usability lab testing uncovered numerous user preferences for redesign of the site, including its content. Conclusions: Current Ontario patients and MDs are not ‘consumers’ of WT information but AD see value in this information for system improvement. As society increasingly uses the internet as an information resource, future patients and MDs will likely utilize WT information in the decision-making for location of care. Supported by Cancer Care Ontario and grant 03110 from the Change Foundation. No significant financial relationships to disclose.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".