Development and Testing a Volunteer Screening Tool for Assessing Community Health Volunteersʼ Motives at Recruitment and Placement in Western Kenya
Bibliographic record
Abstract
Introduction:In times of inadequate resources and rising public demand, social service organizations rely on volunteers to meet needs.In the current human resource for health crisis in Africa there is urgent need for community health volunteers (CHVs).Studies have highlighted problems of high attrition rates leading to high replacement training costs among CHVs.There is need for careful selection of volunteers that can serve long-term, once trained.This study was done to develop a volunteer assessment framework for recruitment of CHVs.The framework is based on identification intrinsic motives for volunteering that have been shown to be associated with long volunteer service.Methods: The assessment tool was developed by searching literature for theory based constructs and assessment items associated with volunteering.These constructs and items were synthesized into a proposed assessment framework.The framework was subjected to face content and construct validation in West Kenyan context in phase 1 of the study.The validated framework was tested for ability to differentiate between long serving volunteers and nonvolunteers matched by gender, age and residence.The 2 groups were presented with test items and asked to record their agreement on a scale of 1 to 5 on the reasons why people volunteer.Results: From literature we identified functional, role identity, and social exchange as theories underpinning volunteering.From these theories we identified 8 constructs to include in a proposed volunteer assessment framework.We tested the framework and although all the eight constructs satisfied internal consistency test only 5: altruism, materialism, social adjustment, esteem enhancement and career development were statistically significantly more associated with either volunteers or non-volunteers.Therefore, only these were included on the final volunteer assessment framework, for identification of long serving volunteers in the local context. Conclusion:We propose a volunteer assessment tool with the 5 constructs and 25 assessment items for identification and recruitment of CHVs, with motives consistent with long-term volunteer service.The final framework consists of altruistic (altruism, social adjustment, esteem enhancement) or egoistic (material gain and career development) constructs with 25 assessment statements.The frame work would able to identify individuals with altruistic motives to include and those with egoistic tendencies to exclude during a volunteer recruitment exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".