A quality improvement project to optimize access to psychosocial care for cancer survivors who experience fear of recurrence
Bibliographic record
Abstract
BACKGROUND: The prevalence of moderate to high levels of fear of cancer recurrence (FCR) in cancer survivors may vary from 22% to 87%, although most are not usually referred to psychosocial support. The After Cancer Treatment Transition (ACTT) clinic in Women's College Hospital (Toronto) provides follow-up care to cancer survivors but in a sample of 2893 patients seen April 2019 to March 2022, only 1.5% were referred to a social worker for psychosocial needs. A single-question screening tool is currently available to screen for FCR. OBJECTIVE: To evaluate the use of the single-question screening tool for FCR among cancer survivors and its impact on social work referrals. RESULTS: Between July and October 2022, 788 patients were seen in the ACTT clinic. Generally, most patients in ACTT are breast cancer survivors (75%), and the remaining survivors are a mix of other cancer types (colorectal cancer, ovarian cancer, thyroid cancer, melanoma). Three hundred thirty (41.9%) ACTT patients completed the single-question screening tool for FCR. Most screened patients were female (96%), the average age was 60 years, and most were diagnosed with breast cancer (90%). Among screened patients, 37 (11%) indicated a moderately severe to high level of FCR and efforts were made to refer these 37 patients to a social worker. In the end, 22 (59.5%) patients with moderately severe/high levels of FCR were offered and accepted referral to a social worker. In comparison to the 1.5% referred to social work (among 2893 patients) prior to FCR screening, referrals increased to 6.7% (among 330 screened). CONCLUSION: Use of a single-question FCR screening tool improved identifying cancer survivors in need of psychosocial support and improved access to a social worker.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".