Health professionals’ involvement in volunteering their professional skills: a scoping review
Bibliographic record
Abstract
Background Volunteering positively affects overall health of both volunteers and recipients through social interaction, support and physical activity. Health professionals’ volunteering has considerable potential to improve health outcomes in communities. Objectives This study aimed to summarize published scientific literature regarding volunteering by health professionals. Method Medine, Embase, Scopus, PsycINFO and CINAHLdatabases were searched to identify eligible studies published between 2010 and 2023. Data on study methods and findings were extracted and synthesized. Results Of the 144 eligible studies, 80 (56%) used quantitative methods, 46 (32%) used qualitative, 18 (12%) used mixed methods and 8 (6%) were interventional. Doctors (74 studies, 51%) and nurses ( n = 40, 28%) were the professions with most reports of volunteering. Half the studies were from USA ( n = 77, 53%), followed by UK ( n = 19, 13%), Canada ( n = 12, 8%), and Australia/New Zealand ( n = 11, 8%). International volunteering in low-to-middle-income countries was reported in 64 studies (44%). Providing service and training were the dominant types of activities ( n = 90, 62.5%), with health promotion reported in only 4 studies (3%). Studies reported positive impact from volunteering, both professionally and personally. Time and family commitments were the main barriers. Enablers, barriers and impact were summarized in a socio-ecological map. Conclusion Health professionals volunteer in diverse activities and report multifaceted benefits. Studies of volunteering interventions could enable new, sustainable approaches to health promotion.
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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 | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".