Ensuring Equity and Inclusion in Virtual Care Best Practices for Diverse Populations of Youth with Chronic Pain
Bibliographic record
Abstract
Poor access to care is a top patient-oriented research priority for youth with chronic pain in Canada, and the COVID-19 pandemic has exacerbated these concerns.Our patientoriented project team engaged with marginalized and racialized youth with chronic pain (Black youth with sickle cell disease, Indigenous youth and youth with complex medical needs) and their families to ensure that best practice recommendations for virtual care are inclusive and equitable.Input provided through virtual round-table discussions improved recommendations for leveraging, implementing and selecting best platforms for virtual care for youth with chronic pain and identified new gaps for future research, practice and policy change. Key Points• Partnership is key to equitable, diverse and inclusive engagement, particularly when engaging with populations or population groups that are marginalized.• Virtual activities both facilitated and hindered equitable, diverse, inclusive and accessible engagement.• Patient engagement offered an opportunity to critically expand on and refine learnings from the scientific literature -in this case, on a rapidly emerging widespread need for virtual care for pain during the COVID-19 pandemic.
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 | 0.076 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".