Using a Virtual Environment to Deliver Evidence-Based Interventions: The Facilitator's Experience
Bibliographic record
Abstract
BACKGROUND: Evidence-based interventions (EBIs) have the potential to maximize positive impact on communities. However, despite the quantity and quality of EBIs for prevention, the need for formalized training and associated training-related expenses, such as travel costs, program materials, and input of personnel hours, pose implementation challenges for many community-based organizations. In this study, the community of inquiry (CoI) framework was used to develop the virtual learning environment to support the adaptation of the ¡Cuídate! (Take Care of Yourself!) Training of Facilitators curriculum (an EBI) to train facilitators from community-based organizations. OBJECTIVE: The purpose of this study was to examine the feasibility of adapting a traditional face-to-face facilitator training program for ¡Cuídate!, a sexual risk reduction EBI for Latino youth, for use in a multi-user virtual environment (MUVE). Additionally, two aims of the study were explored: the acceptability of the facilitator training and the level of the facilitators' knowledge and self-efficacy to implement the training. METHODS: A total of 35 facilitators were trained in the virtual environment. We evaluated the facilitators' experience in the virtual training environment and determined if the learning environment was acceptable and supported the acquisition of learning outcomes. To this end, the facilitators were surveyed using a modified community of inquiry survey, with questions specific to the Second Life environment and an open-ended questionnaire. In addition, a comparison to face-to-face training was conducted using survey methods. RESULTS: Results of the community of inquiry survey demonstrated a subscale mean of 23.11 (SD 4.12) out of a possible 30 on social presence, a subscale mean of 8.74 (SD 1.01) out of a possible 10 on teaching presence, and a subscale mean of 16.69 (SD 1.97) out of a possible 20 on cognitive presence. The comparison to face-to-face training showed no significant differences in participants' ability to respond to challenging or sensitive questions (P=.50) or their ability to help participants recognize how Latino culture supports safer sex (P=.32). There was a significant difference in their knowledge of core elements and modules (P<.001). A total of 74% (26/35) of the Second Life participants did agree/strongly agree that they had the skills to deliver the ¡Cuídate! CONCLUSIONS: The results showed that participants found the Second Life environment to be acceptable to the learners and supported an experience in which learners were able to acquire the knowledge and skills needed to deliver the curriculum.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".