Community-based clinic volunteering: an evaluation of the direct and indirect effects on the experience of health science college students
Bibliographic record
Abstract
BACKGROUND: The present study was conducted in a multi service-learning, student managed and operated, community-based clinic. Its aim was to measure the direct and indirect effects of how proximal factors (i.e., 'management', 'support received', 'duration of involvement', and 'average time spent per month') and mediators (i.e., 'training received', 'motivation', and 'commitment') influence distal outcomes (i.e., 'performance', 'satisfaction', and 'overall experience') within a volunteer organization. METHODS: Participants were recruited through the use of an email list server. An online survey was used containing multi-item measures from validated scales. Data were collected from 170 volunteers from July to August 2013. Data analysis used a structural equation modeling (SEM) framework for the estimation of direct and indirect effects on constructs and variables of interest. Only statistically significant relationships were reported at p < 0.05. RESULTS: In this study, there are several direct effects worthy of note. First, the proximal factor of 'management' plays an important role in influencing the mediators of 'motivation' (standardized beta = 0.55) and 'training received' (0.65) by the student volunteers but has a relatively small impact on their 'commitment' (0.39) to the organization. Second, the mediator of 'motivation' proved to have the strongest impact on the distal outcome of volunteer 'performance' and 'satisfaction' levels (0.41 and 0.58 respectively), whereas 'commitment' (0.44) was the key in determining their 'overall experience' with the organization. These results in turn, help contextualize the indirect effects observed in our study. Namely, the proximal factor of 'management' played a distinctive role in influencing the distal outcomes of volunteer 'performance' (0.32) and 'overall experience' (0.66), whereas the organizational 'support received' by the volunteers was key to their 'satisfaction' (0.21). CONCLUSIONS: The findings of the present study shed light into the direct and indirect effects of how proximal factors and mediators, influence student volunteers distal outcomes within a community-based clinic. These results provide useful information and serve as a valuable tool to higher education (curriculum experts, accreditation specialists, students, faculty and administrators) and non-profit community organizations (clients, staff and managers) in their efforts to improve student volunteer satisfaction and performance outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".