<i>Let all know</i> : insights from a digital storytelling facilitator training in Uganda
Bibliographic record
Abstract
BACKGROUND: Digital storytelling (DST) is a participatory, arts-based methodology that facilitates the creation of short films called digital stories. Both the DST process and resulting digital stories can be used for education, research, advocacy, and therapeutic purposes in public health. DST is widely used in Europe and North America, and becoming increasingly common in Africa. In East Africa, there is currently limited in-country DST facilitation capacity, which restricts the scope of use. Through a Ugandan-Canadian partnership, six Ugandan faculty and staff from Mbarara University of Science and Technology participated in a pilot DST facilitation training workshop to enhance Ugandan DST capacity. OBJECTIVE: This Participatory Action Research (PAR) study assessed the modification of DST methodology, and identified the future potential of DST in Uganda and other East African settings. METHODS: In the two-week DST Facilitator Training, trainees created their own stories, learned DST technique and theory, facilitated DST with community health workers, and led a community screening. All trainees were invited to contribute to this study. Data was collected through daily reflection and journaling which informed a final, post-workshop focus group where participants and researchers collaboratively analyzed observations and generated themes. RESULTS: In total, twelve stories were created, six by trainees and six by community health workers. Three key themes emerged from PAR analysis: DST was a culturally appropriate way to modernize oral storytelling traditions and had potential for broad use in Uganda; DST could be modified to address ethical and logistical challenges of working with vulnerable groups in-country; training in-country facilitators was perceived as advantageous in addressing community priorities. CONCLUSION: This pilot study suggests DST is a promising methodology that can potentially be used for many purposes in an East African setting. Building in-country DST facilitation capacity will accelerate opportunities for addressing community health priorities through amplifying local voices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".