Negative Health Impacts of Navigating the Healthcare System for Musculoskeletal Conditions: Healthcare User Perspectives
Bibliographic record
Abstract
Objectives Musculoskeletal (MSK) disorders pose a significant public health challenge, affecting patient quality of life, productivity, and healthcare costs. While the negative impacts of living with chronic MSK conditions are well-studied, the health impacts associated with navigating the healthcare system for MSK care remain underexplored. The objective of this research is to explore patients’ experiences of navigating the healthcare system for (soft tissue) knee, shoulder, and lower back conditions and understand the connections between aspects of health system navigation and negative health impacts. Methods This qualitative study used interpretive description methodology to explore patients’ experiences with the MSK healthcare system for knee, shoulder, and lower back issues. Data was collected from November 2022 to December 2023 through 1-on-1 semi-structured telephone interviews. Interviews were transcribed and analyzed using framework analysis. The study received ethics approval from the University of Calgary Conjoint Health Research Ethics Board (REB22-0881). Results 73 individuals (61.6% female, 47.5 median age) participated in the study. Over half of participants experienced negative health impacts due to navigating the MSK healthcare system. Reported issues included physical decline (debilitation, new symptoms), mental health challenges (depression, anxiety, loss of self, disempowered), and prolonged suffering (pain, suicidal ideations). The contributing factors were lack of access to effective treatments, lack of compassionate care, perpetual waiting, care discontinuity, and lack of provider knowledge. Conclusion The findings highlight systemic issues that worsen patient health and hinder well-being, emphasizing the need for comprehensive MSK healthcare reform. A well-integrated, patient-centered system is crucial for optimizing care delivery and empowering patients in their MSK care. Policymakers and healthcare administrators must address these systemic barriers to improve accessibility, care integration, and continuity. Future research should assess the systemic impacts of healthcare navigation on patient health and satisfaction.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".