A qualitative descriptive study of patients’ experiences of a radiation skin reaction associated with treatment for a head and neck cancer
Bibliographic record
Abstract
The majority of patients receiving radiation therapy will experience a radiation skin reaction, ranging from mild erythema to severe moist desquamation. The anticipated skin reaction will vary by cancer diagnosis, dose of radiation, size of treatment field, and other treatment-and patient-related factors. Recently, research has dispelled long-held myths about avoiding skin care products prior to treatment, and the use of deodorant or antiperspirant during treatment for breast cancer and any potential for an increased skin dose with the use of skin care products. Patients no longer are restricted in using these products. Most of the research regarding skin reactions due to radiation has been conducted with women with breast cancer. No research has been found on the experience of a radiation skin reaction in patients with head and neck cancer (HNC), who often get the most severe skin reactions across all patient groups. Using the qualitative interpretive descriptive approach based on the methodological work by Thorne (1997), nine participants were interviewed about their experience of having a radiation skin reaction resulting from their treatment for HNC. Three themes emerged from the interviews, including 1) the symptom experience, 2) comparison to others, and 3) the experience of support and information. Implications include recommendations for practice and patient education.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".