Effective practices of international volunteering for health: perspectives from partner organizations
Bibliographic record
Abstract
BACKGROUND: The demand for international volunteer experiences to promote global health and nutrition is increasing and numerous studies have documented the experiences of the international volunteers who travel abroad; however, little is known about effective practices from the perspective of partner organizations. This study aims to understand how variables such as the skill-level of volunteers, the duration of service, cultural and language training, and other key variables affect partner organizations' perceptions of volunteer effectiveness at promoting healthcare and nutrition. METHOD: This study used a cross-sectional design to survey a convenience sample of 288 volunteer partner organizations located in 68 countries. Principle components analyses and manual coding of cases resulted in a categorization of five generalized types of international volunteering. Differences among these types were compared by the duration of service, skill-level of volunteers, and the volunteers' perceived fit with organizational needs. In addition, a multivariate ordinary least square regression tested associations between nine different characteristics/activities and the volunteers' perceived effectiveness at promoting healthcare and nutrition. RESULTS: Partner organizations viewed highly-skilled volunteers serving for a short-term abroad as the most effective at promoting healthcare and nutrition in their organizations, followed by slightly less-skilled long-term volunteers. The greatest amount of variance in perceived effectiveness was volunteers' ability to speak the local language, followed by their skill level and the duration of service abroad. In addition, volunteer training in community development principles and practices was significantly related to perceived effectiveness. CONCLUSION: The perceptions of effective healthcare promotion identified by partner organizations suggest that program and volunteer characteristics need to be carefully considered when deciding on methods of volunteer preparation and engagement. By better integrating evidence-based practices into their program models, international volunteer cooperation organizations can greatly strengthen their efforts to promote more effective and valuable healthcare and nutrition interventions in partner communities.
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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 | 0.011 | 0.012 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".