The Unmet Needs of Palliative Care Among Young and Middle-Aged Patients with Advanced Cancer: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore the unmet palliative care needs among young and middle-aged (YMA) Chinese patients with advanced cancer. METHODS: We used the principle of maximum difference. A total of 16 YMA patients with advanced cancer from cancer hospital were recruited. Semi-structured, in-depth, and face-to-face interviews were conducted from 28 August 2023 to 23 October 2023. The recorded audio of each interview was typed into Word software with each personal code. The interview transcripts were coded using the method of inductive content analysis. RESULTS: Four themes and 14 sub-themes were identified in participants' descriptions of care needs: (1) symptom management needs: need for pain relief, need for anti-emetics, and need for aid in managing fatigue; (2) psychological support needs: help reducing fear of pain, help achieving a better death, and help with parents' negative reactions; (3) social support needs: taking care of children, emotional support from family members, consultation and emotional support from other cancer patients, and company and guidance of healthcare personnel; (4) information needs: better understanding of disease trajectory and future care needs, better access to palliative care information, and more participation in medical decision-making. CONCLUSIONS: According to the results of this study, the unmet palliative care needs of YMA patients with advanced cancer are diverse, but they have not been fully recognized and met. Therefore, medical staff should develop effective management strategies and explore patients' needs in an all-around way. Future studies will further develop the scale of unmet needs for palliative care to accurately identify needs and improve patients' quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".