Dental Service and Resource Needs during COVID-19 among Underserved Populations
Bibliographic record
Abstract
INTRODUCTION: In response to the coronavirus disease 2019 (COVID-19) outbreak, dental services in British Columbia, Canada, were restricted to urgent and emergency cases between March 16 and May 18, 2020. It is unclear how the curtailment of oral health services has affected underserved populations who already often have limited access to dental care due to cost, fear, stigma, and discrimination. OBJECTIVES: To explore the experiences of underserved populations and their community organizations when accessing oral health services and information in British Columbia and identify their coping mechanisms employed during the curtailment of oral health care services. METHODS: Semistructured, remote interviews were conducted with 13 staff and 18 members from 6 community-based organizations. These organizations serve men and women with a history of incarceration and/or experiencing poverty and homelessness, persons living with human immunodeficiency virus/AIDS, adults living with mental illness, and older adults in long-term care facilities. The interviews were audio-recorded, transcribed verbatim, and coded for emerging themes using NVivo 12 software. Thematic analysis was performed. RESULTS: The pandemic raised concerns and hesitancy among underserved populations and further reduced access to care. In turn, those with unmet dental needs resorted to coping mechanisms, including turning to community support or medical services, self-management of dental issues, and not dealing with dental issues altogether. Community organizers and members outlined needed resources such as assistance navigating the dental care system, having a contact for dental-related questions, and member preparation for dental service changes, while emphasizing the importance of positive relationships with dental providers. CONCLUSION: Underserved populations who already face barriers to oral health care services experienced increased difficulty in addressing their oral health needs and concerns during the beginning of the COVID-19 pandemic. Strategies aimed at reaching out to this population and those who support them are needed to help mitigate negative coping strategies and increased oral health disparities. KNOWLEDGE TRANSFER STATEMENT: This study depicts ways of addressing unmet oral health-related issues during the COVID-19 pandemic for underserved populations and their community organizations with policy implications as well as practical strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".