<i>Nishtam Niwiipitan</i> (My First Teeth): Oral Health Digital Stories from Urban Indigenous Parents
Bibliographic record
Abstract
PURPOSE: To develop oral health-related digital story videos through interviews with Indigenous parents who shared their experiences in dealing with early childhood caries (ECC) in their children. METHODS: Indigenous parents in Winnipeg, Manitoba, Canada, were recruited from community programs from October to December 2019 as part of the Nishtam Niwiipitan (My First Teeth), a community-based participatory research study that builds on an ECC intervention. A twofold qualitative narrative approach to data collection was used: 1) interviewing participants and creating digital stories and 2) taking part in the postfilming feedback interviews. Participants were interviewed via video in a semistructured format sharing their experiences and attitudes about caring for children with ECC and the challenges faced seeking dental care for the disease. The stories were drawn from parents in 3 predetermined groups: those with 1) children who had undergone dental surgery under general anesthesia, 2) children who had received silver diamine fluoride as an alternative to surgery to manage ECC, and 3) caries-free children. Prior to editing, the narrated stories were transcribed verbatim and analyzed thematically. The postfilming interview transcripts were also analyzed and coded for key themes. RESULTS: Six parents and 1 grandparent, all of whom self-identified as Indigenous (First Nations or Métis) and cared for children aged <6 y, created the digital stories. Three key themes emerged from the postfilming interviews: ability to share, ability to help, and ability to change. Participants felt important, optimistic, and motivated throughout the process of making their digital stories. CONCLUSION: Digital storytelling offered First Nations and Métis parents a unique opportunity to share their experiences caring for children with ECC with the wider public. These videos can be incorporated into oral health promotion and ECC intervention programs as a culturally appropriate method for reaching Indigenous families. KNOWLEDGE TRANSFER STATEMENT: The use and development of digital storytelling for oral health promotion have great potential for spreading awareness and sharing knowledge with Indigenous parents/caregivers about their children's oral health and care practices. This health promotion tool is congruent with Indigenous ways of knowing, as Indigenous communities have a long tradition of oral history. The videos produced for this study will assist with oral health promotion efforts to address the high rates of early childhood caries in Indigenous communities in Canada.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".