Quality of life and quality of care in cancer survivors: Fear of cancer recurrence—Preliminary results of a transcultural availability of validated tools.
Bibliographic record
Abstract
e19675 Background: Cancer survivors are faced with physical and psychological problems. In follow-up care we have to give greater attention to the quality of life. The fear of cancer recurrence (FCR) is one the most disturbing aspects, resulting in pointless examinations and one of the most prevalent areas of unmeet needs. Yet few efforts have been made to better understand this phenomenon and to work out a validated measures. The goal of this study was to develop the Italian version of the Fear of Cancer Recurrence Inventory (FCRI). Methods: The FCRI is a multidimensional 42-items questionnaire evaluating seven fear components: Triggers, Severity, Psychological distress, Functioning impairments, Insight, Reassurance and Coping strategies. It was initially worked out and validated with French-speaking Canadian cancer survivors (Simard & Savard Supp Care Cancer 2009 17:241-251). A forward-backwards procedure was used to translate the original FCRI into Italian, then it was pilot tested in fifty Italian cancer survivors to assess face and content validity. Results: The partecipants were able to identify correctly the general content being measured (i.e. good face validity). Italian FCRI was considered to be clear, useful, simple to use and no time consuming; it could help to investigate the fears of patients facilitating the communication with physicians. It was found to be distressful only by one partecipant, but this was associated with a recent death of a relative. Partecipants were happy to collaborate in a project that could help other patients. 16% of interviewed suggested questions about God in coping strategies; in their mind God represents an important support to fight their fears. Conclusions: These findings provide preliminary evidence about the usefulness and the utility of FCRI in people with different culture. Further studies are needed to assess its validity and its transcultural availability. If cancer treatments move from cytotoxic agents to molecular ones, Physicians need to take care of survivors exploring more global perspectives. FCRI will be released at sebastien.simard@criucpq.ulaval.ca 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".