Exploring unmet healthcare needs, healthcare access, and the use of complementary and alternative medicine by chronic pain sufferers- An analysis of the National Population Health Survey
Bibliographic record
Abstract
Background: Chronic pain is a condition nurses encounter in their practice often; estimated to affect 1 in 5 Canadian adults, resulting in significant disability, a deleterious impact on health and quality of life, and a large financial and operational burden on the health care system. It is a complex and multifactorial phenomenon that despite research efforts remains poorly understood. Consequently, the focus of chronic pain treatment targets the managementof pain to improve quality of life and reduce suffering as much as possible, rather than a curative approach. Chronic pain has been recognized as one of the most pervasive and challenging conditions to manage by health professions. Subsequently, the treatment of chronic pain is considered an effectiveness gap, or a clinical area where current conventional treatments are not fully effective. As a result, more chronic pain sufferers are turning to Complementary and Alternative Medicine (CAM) to manage their pain, the use of which has increased significantly over the past few decades. Literature suggests unmet healthcare needs can motivate CAM use, and this is directly related to the concept of healthcare access. To the researcher's knowledge, the relationship between CAM use, unmet healthcare needs and healthcare access has not yet been studied within the context of Canadians with chronic pain.\nObjectives: The purpose of this study was to explore the relationship between healthcare access, unmet healthcare needs, and CAM use in adults with chronic pain.\nMethods: A secondary analysis of data from Cycle 9 of the National Population Health Survey. The Behavioural Model of Health Services Utilization was used as a theoretical lens to conduct a binary logistic regression analysis and related descriptive statistics of the sample.\nResults: When controlling for demographics and health status indicators, the presence of unmet healthcare needs was found to predict the use of complementary and alternative medicine (p < 0.001). Healthcare access was not statistically significant in the model. Other statistically significant predictors of CAM use in adults with chronic pain were sex, education, income, employment, and restriction of activities.\nConclusion: Understanding healthcare access and unmet healthcare needs is critical to developing service improvement strategies. This study indicates that people may be engaging in CAM due to shortcomings of the conventional health care system. This has implications for policymakers and healthcare professions to develop strategies to improve chronic pain management. These findings also support the necessity of more research into establishing safe and effective CAM practices via regulatory standards and a sound evidence base to support these therapies.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".