A qualitative exploration of fear of cancer recurrence (FCR) amongst Australian and Canadian breast cancer survivors
Bibliographic record
Abstract
PURPOSE: Few studies have explored coping strategies used by cancer survivors to deal with fear of cancer recurrence (FCR), and little research has been conducted on the specific content of recurrence fears. This study aims to qualitatively explore the strategies used by younger breast cancer survivors to cope with FCR and whether women with low, medium and high levels of FCR employ different coping strategies. An additional aim was to understand the specific content of worst recurrence fears. METHOD: Twenty Australian and 10 Canadian women aged ≤ 45 years diagnosed with stages 0-II disease at least 1 year prior completed telephone interviews. The transcripts of audio-taped interviews were analysed using the qualitative methodology of transcendental realism. RESULTS: Women with higher FCR described using distraction and avoidance and fewer coping skills. The fear of death was a common worst fear at all levels of FCR. However, participants with higher FCR described more elaborate fears of death often involving themes of pain and suffering. Cross-cultural differences were not observed. CONCLUSIONS: Women with higher FCR report using fewer and more avoidance-based coping techniques. Whilst many participants feared death, those with higher FCR reported more elaborate death fears. Women with high levels of FCR may benefit from learning a greater repertoire of coping skills. Understanding the specific content of FCR can help refine existing psychological treatment protocols for FCR. Implications for FCR treatment are discussed.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".