The Exclusivity of ‘Vulnerable’: Exploring How a Canadian Community Dental Clinic Defines and Describes Its Targeted Population
Bibliographic record
Abstract
OBJECTIVES: Addressing inequitable oral health access is a global priority. In Canada, community dental clinics (CDCs) play a crucial role in this endeavour, yet limited resources necessitate strategically targeting communities for interventions. Various methods exist for defining communities and measuring outcomes, but how CDCs determine their target populations is under-researched. This study aimed to explore how decision-makers planning an inner-city CDC define the population they intend to serve. METHODS: Data was collected through key informant interviews, document analysis, and field observations. Purposive sampling was employed to select key informants and documents related to clinic planning and design. The researcher was immersed in the data throughout the study, which underwent inductive content analysis facilitated by NVivo software. RESULTS: Analysis included semi-structured key informant interviews (n = 11), textual data from public sources and key informants (n = 9), and field observations totalling 275 hours over 1 year (2020-2021). Key informants agreed that the clinic served a "vulnerable" population, but definitions of "vulnerable" varied. Initial coding revealed two distinct patient groups with differing portrayals. Based on five patient characteristics Sossauer et al. (2019) described, one group was portrayed positively, while the other was depicted negatively. CONCLUSIONS: This study underscores the necessity of establishing a shared understanding of "vulnerability" in interdisciplinary projects like the CDC examined here. Assumptions about community groups hold significant consequences, shaping resource allocation, programme implementation, and policy decisions. It is imperative to critically assess who is making these decisions, their conception of vulnerability, and the repercussions of these beliefs on affected communities.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.037 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".