Perceived usefulness of new technologies in palliative care volunteering: mixed-methods study with stakeholders
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, face-to-face volunteer support for patients and families was not possible, making it necessary to explore alternative ways of reducing distress. New technologies emerged as a valuable resource, facilitating communication and information exchange, and supporting volunteer tasks. OBJECTIVE: To explore the perceived usefulness of new technologies in volunteering among different stakeholders (patients, relatives, professionals, and volunteers), and to examine how these perceptions relate to participants' technological profiles. DESIGN: A cross-sectional mixed-methods study was conducted to explore attitudes and preferences toward new technologies. Quantitative data were analyzed descriptively and through regression models, while qualitative data were examined using thematic analysis. METHODS: Participants were recruited through consecutive non-probabilistic sampling and included patients, relatives, healthcare professionals, and volunteers from various care settings. Quantitative measures assessed perceptions of usefulness, benefits, barriers, and satisfaction with volunteering, alongside the TechPH tool to profile technological attitudes. Qualitative data were collected through interviews and focus groups using open-ended questions to explore the perceived usefulness of new technologies in palliative care volunteering. Quantitative analysis involved descriptive statistics, Pearson correlations, ANOVA, and multiple linear regression. Qualitative data were analyzed using thematic analysis. RESULTS: A total of 402 individuals participated: 50 patients, 45 relatives, 136 professionals and 171 volunteers. Perceived usefulness of new technologies varied: 50% of patients, 63.6% of relatives, 77.8% of professionals, and 78.2% of volunteers found them beneficial. Three themes emerged from qualitative analysis: difficulties in new technologies use (mainly among patients), perceived benefits (e.g., enhanced communication), and the need for volunteer training in digital skills. CONCLUSIONS: All stakeholder groups recognized new technologies as useful for volunteer support in palliative care, with the highest perceived usefulness among professionals and volunteers. However, professionals were the least involved in volunteer support. Patients reported the lowest acceptance, preferring a hybrid model in which technology complements, but does not replace, in-person support.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".