Pain Reports among a Complex Chronic Care Population in Canada
Bibliographic record
Abstract
The present dissertation examines pain reports among a complex chronic disease (CCD) population of adults from complex chronic care (CCC) facilities across Ontario, Canada. The literature review provides an overview of multimorbidity, complex chronic disease, acute and chronic pain, and the interactions between these constructs. The present dissertation comprises three studies that examine reports of pain among the CCD population, with each study drawing upon data from the Canadian Institute of Health Information (CIHI) Continuing Care Reporting System (CCRS) dataset, obtained through the Graduate Student Data Access Program (GSDAP). The first study investigates combinations of multimorbidity associated with pain. Specifically, pain prevalence, intensity and frequency are examined in relation to medical disease categories, as well as, combinations of two to five of the most prevalent multimorbid disease categories among this adult, CCD population. This study provides an overview of the types of disease combinations that are particularly problematic in regard to pain among this population. Results indicate that the top five most prevalent combinations of multimorbid disease patterns are endocrine/metabolic/nutrition, heart/circulation, musculoskeletal, neurological and psychiatric/mood. Building further upon these findings, Studies 2 and 3 examine pain reports from specific disease categories in the greater CCD population. Study 2 examines the phenomenon of hypertension-associated-hypoalgesia, investigating the difference in pain reports between Ontario CCC residents with and without hypertension. Optimal propensity matched groups were compared for differences on various pain report scales and results indicated that residents with hypertension experience less pain (hypoalgesia) than residents without hypertension. Study 3 investigates pain reports in another sub-group of the CCD population; specifically, those with missing limbs. Similar to Study 2, propensity matching was utilized to create comparably matched groups of residents with and without missing limbs. Findings from this study are consistent with existing literature that individuals with amputations are more likely to report greater pain than those without. In both studies 2 and 3, females were more likely to report pain than males, consistent with extensive pain literature in this area. \nAll three studies benefit from the use of a reliable, comprehensive dataset that allows for the inclusion of multiple control variables and a large, diverse and generalizable sample size. A limitation permeating each study, however, is the pain reporting scale and vague disease descriptions. These strengths and limitations are addressed in detail throughout each study, as well as in the General Discussion of the dissertation. The contributions of each of the three studies presented in this dissertation allows for more information about the demographic, medical and pain experiences of this unique population, which in turn can be used in a clinical context to inform training for current and future health care providers, as well as in the policy context to guide intervention efforts. Future research that identifies the successful approaches to managing CCD and pain would be invaluable to delivering individualized, adaptive and patient-centered care for this growing population. This research is necessary for ensuring that this significant patient population is receiving the highest quality of care from multidisciplinary health care teams.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".