Using Maslow's hierarchy to highlight power imbalances between visiting health professional student volunteers and the host community: An applied qualitative study
Bibliographic record
Abstract
BACKGROUND: Health professional students from high-income countries increasingly participate in short-term experiences in global health (STEGH) conducted abroad. One common criticism of STEGH is the inherent power differential that exists between visiting learners and the local community. To highlight this power differential, this paper explores perceived benefits as described by volunteer and community respondents and applies Maslow's hierarchy of needs to commonly identified themes in each respondent group. METHODS: A semistructured survey was used to collect qualitative responses from both volunteers and community members located in a Dominican Republic community, that is, a hotspot for traditionally conducted STEGH. Thematic analysis identified themes of perceived benefits from both respondent groups; each group's common themes were then classified and compared within Maslow's hierarchy of needs. RESULTS: Each respondent group identified resource provision as a perceived benefit of STEGH, but volunteer respondents primarily focused on the provision of highly-skilled, complex resources while community respondents focused on basic necessities (food, water, etc.) Volunteer respondents were also the only group to also mention spiritual/religious/life experiences, personal skills development, and relationships as perceived benefits. Applying Maslow's hierarchy thus demonstrates a difference in needs: community respondents focused on benefits that address deficiency needs at the bottom of the hierarchy while volunteers focused on benefits addressing self-transcendence/actualization needs at the top of the hierarchy. CONCLUSIONS: The perceived difference in needs met by STEGH between volunteers and the host community within Maslow's hierarchy may drive an inherent power differential. Refocusing STEGH on the relationship level of the hierarchy (i.e., focusing on partnerships) might help mitigate this imbalance and empower host 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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".