Symptom monitoring and the uncertain threat of disease recurrence: A deductive thematic analysis with adolescent and young adult (AYA) cancer survivors.
Bibliographic record
Abstract
147 Background: Symptom monitoring plays an important role in both the physical and psychological challenges of surviving cancer. Anecdotally, cancer survival is characterized by uncertainty, symptom-related fear, and the interpretation of normal bodily sensations as symptomatic of cancer recurrence. This fear may lead to over-vigilance of benign bodily sensations, increasing anxiety and decreasing quality of life. Yet, there are few studies investigating how cancer survivors interpret and make sense of post-cancer symptoms. These studies are needed to guide considerations for clinical practice and the development of supportive interventions. Methods: We conducted in-depth semi-structured interviews with 18 AYA cancer survivors about how they interpret, manage, and respond to physical symptoms during survivorship. Participants were 15-25 years old. The sample was diverse in terms of disease history, ethnicity (8 Hispanic), and gender (9 females, 1 nonbinary). We conducted thematic analysis using a deductive coding scheme that was developed using our Cancer Threat Interpretation (CTI) theoretical model of cognitive, affective, and behavioral processes in post-cancer symptom perception. Results: AYA cancer survivors reported experiencing anxiety in the face of common physical sensations. These sensations were often interpreted as potential signs of recurrence or as late effects of treatment. Survivors most commonly reported worries about pain and fatigue, but also other sensations such as breathlessness. We coded participant transcripts into the following themes: biased attending towards symptoms, biased interpretations of symptoms as threatening, fear and worry about symptoms, and behavioral response to symptoms. In addition, we generated a new theme that was not previously captured in the CTI model: trust in the body. Conclusions: Knowing how to appraise and respond to symptoms such as pain is a challenge for AYA cancer survivors. This rich qualitative data provides support for the CTI model and suggests future directions. The results of this study can inform how clinicians talk to their patients about symptom monitoring after treatment ends.
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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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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 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".