Chronic pain and marginalization in a time of pandemic
Bibliographic record
Abstract
Context: The current pandemic has drawn attention to the importance, failings and weaknesses of the public health system in promoting and maintaining health for all and especially vulnerable groups and those living with serious chronic conditions; this includes those living with chronic pain. COVID-19 response measures are creating barriers to accessing services for those living with pain, such as pain management services and supervised injection sites, which is contributing to a spike in overdoses. Objective: Our project examines how people living with chronic pain and marginalization persevere during COVID-19. Our overarching research question is “how do participants with chronic pain, who also live with poverty, addictions and mental illness, manage their lives in the time of this pandemic?”. Study Design: Our research is conducted using applied qualitative methods, drawing on an approach known as institutional ethnography, which uses people’s everyday experiences as the starting point for an exploration of the often invisible social relations that orient those experiences. Setting or Dataset: Given the pandemic restrictions, participants were recruited in online spaces (e.g. Twitter and Kijiji) and through the existing networks of our multi-disciplinary study team and interviewed by telephone. Interviews were approximately 60-90 minutes in length depending on how much information the participants wanted to share. Population Studied: Our study focused on people living in Canada over the age of 18, who speak fluent English and who self-identify as living with chronic pain, and marginalization. Results: Our findings highlight the complex ways in which people have been impacted by policies, both negatively and positively. For some people, the pandemic was an equalizer, insofar as they already lived lives marked by isolation and believed that others now knew “how that felt”. Others described their mistrust of or inability to adhere to public health recommendations as causing additional stress. Conclusions: By identifying the strategies people use to persevere we are able to provide information for targeted interventions to address structural vulnerabilities and social determinants of health. This knowledge is critical to help better (re)design our systems and inform decision-making across jurisdictions in Canada to ultimately strengthen our public health, health and social care systems now and into the future.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".