Cancer Care Ontario's experience with implementation of routine physical and psychological symptom distress screening
Bibliographic record
Abstract
OBJECTIVE: In late 2006, Cancer Care Ontario launched a quality improvement initiative to implement routine screening with the Edmonton Symptom Assessment System (ESAS) for cancer patients seen in fourteen Regional Cancer Centres throughout the province. METHODS: A central team: created a provincial project plan and management and evaluation framework; developed common tools and provided expert coaching and guidance, provincial data analysis, progress reporting and program evaluation. Regional Steering Committees and Improvement teams were accountable for planning and coordination within each region and supported by a funded Regional Improvement Coordinator. A hybrid model for quality improvement facilitated process improvements and uptake of screening. RESULTS: Challenges to implementation included: lack of consensus on the chosen screening tool, lack of guidance for assessment or management of high scores, concern of inadequate time or resources to address issues identified by the screening, data entry was labour intensive, resistance to change and challenges to the traditional care model. Essential components for success were: centralized project management, a person dedicated to implementation of the project locally, clinical champions, clearly identified aims, monthly regional data reporting and implementation of quality improvement methodologies with expectations for performance. To achieve screening aims many centres engaged all members of the team, examined the roles of the different members and reorganized workflow and responsibilities and changed booking times. In March 2010, approximately 25,000 ESAS's were completed in the regional cancer centres across Ontario, with 60% of lung cancer patients and almost 40% of all other cancer patients who visited the Regional Cancer Centres screened. CONCLUSION: Routine physical and psychological distress screening is possible within regional cancer centres. Although considerable effort and investment is required, it is worthwhile as it helps create a culture that is more patient-centered.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".