Implementation of the Distress Assessment and Response Tool at the Kuwait Cancer Control Centre
Bibliographic record
Abstract
Abstract Background: This report describes the implementation of a comprehensive distress screening program at the Kuwait Cancer Control Center (KCCC), the first such initiative in the Middle East. A Kuwait-adapted version of the Distress Assessment and Response Tool (K-DART) was used in this screening program. Methods: Paper-based K-DART surveys were piloted in the lymphoma clinic at KCCC in July 2013, followed by gradual hospital-wide expansion. K-DART included patient-reported outcome measures to assess cancer-related physical and emotional symptoms and practical problems. English and Arabic translations of these measures were used. Trained nurses administered K-DART and followed up with the oncologist for patients with moderate-to-high levels of distress. Descriptive data are reported for prevalence of distress, psychosocial oncology program (PSOP) referral rates, and patient and staff satisfaction with K-DART. Results: A total of 1,153 K-DART surveys were completed by 618 patients in the pilot lymphoma clinic, with screening rates increasing from 33.5% to 75.3% over the first 12 months of implementation. Among all K-DART completers, 85/618 (13.8%) were referred to PSOP, whereas only 1/955 (0.1%) of K-DART noncompleters were referred to PSOP. After hospital-wide expansion of screening, a total of 2,017 patients completed K-DART in the first year of implementation. Both patients and physicians reported high satisfaction with K-DART, which was reported to enhance patient–physician communication and improve clinical care. Conclusion: Implementation of K-DART in a Middle Eastern country is feasible and facilitates a more comprehensive approach to cancer care, contributing to the successful establishment of a PSOP at the KCCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".