Patients’ quality of life during active cancer treatment: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Patients' quality of life has become a major objective of care in oncology. At the same time, it has become the object of increasing interest by researchers, working with both quantitative and qualitative methods. Progress in oncology has enabled more patients to survive longer, so that cancer is increasingly often a chronic disease that requires long-term treatment that can have negative effects on patients' quality of daily life. Nonetheless, no qualitative study has explored what patients report affects their quality of daily life during the treatment period. This study is intended to fill this gap. METHODS: We conducted a multicenter qualitative study based on 30 semi-structured interviews. Participants, purposively selected until data saturation, had diverse types of cancer and had started treatment at least 6 months before interview. Data were examined by thematic analysis. RESULTS: Our analysis found two themes: (1) what negatively affected for patient's quality of daily life during the treatment period, a question to which patients responded by talking only about the side effects of treatment; and (2) what positively affected their quality of daily life during the treatment period with three sub-themes: (i) The interest in having -investing in - a support object that can be defined as an object, a relationship or an activity particularly invested by the patients which makes them feel good and makes the cancer and its treatment bearable, (ii)The subjective perception of the efficacy of the antitumor treatment and (iii) the positive effects of relationships, with friends and family, and also with their physician. CONCLUSIONS: Patients must be involved in their care if they are to be able to bear their course of treatment and find ways to endure the difficult experience of cancer care. The support object represents an important therapeutic lever that can be used by their oncologists. They should be interested in their support objects, in order to support the patients in this investment and to help them to maintain it throughout the health care pathway. Furthermore, showing interest in this topic, important to the patient, could improve the physician-patient relation without using up very much of the physician's time.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".