A mixed-methods exploration on whether and how community health navigators impact the mental health of adults with chronic health conditions in ENCOMPASS
Bibliographic record
Abstract
Background: Living with chronic health conditions (CHCs) can be distressing due to impacts on quality of life. People with CHCs are more susceptible to mental illness such as anxiety or depression. The Community Health Navigator (CHN) program being tested in the Enhancing Community Health Through Patient Navigation, Social Advocacy, and Social Support (ENCOMPASS) study helps patients living with CHCs address health burdens. CHNs are Community Health Workers who provide patient navigation, and address barriers to care. They support patients by facilitating communication with care providers, connecting them to resources, encouraging care plan adherence, providing advice, education, and emotional support, and helping carry out health-related goals. CHN support may address anxiety and depression, and this study is an opportunity to understand this impact. Purpose: To explore, using a convergent mixed-methods design, whether and how the CHN intervention impacts anxiety and depression of adult patients with CHCs enrolled in the ENCOMPASS study at a Primary Care Network in Calgary, Alberta. Methods: The quantitative portion of this study used anxiety and depression scores from the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9), respectively, administered at baseline, 6- and 12-months. The scores of CHN intervention and control patients were compared at 6- and 12-months using Analysis of Covariance (ANCOVA). The qualitative portion used a qualitative descriptive approach where inductive content analysis was used to analyze transcripts from semi-structured interviews with CHNs and patients, and CHN case notes. The analysis sought to explain the quantitative results and explore how CHNs addressed patients’ mental health concerns. Results: Out of 183 patients enrolled in the ENCOMPASS study, between 140 and 149 patients were included in the quantitative analyses, depending on data completeness for each outcome. After data transformation to address assumptions violations, there were no significant differences in anxiety and depression scores between intervention and usual care (control) patients at either 6 or 12 months after enrolment (p > 0.05). The qualitative analysis provided insight into these results, revealing that CHNs faced challenges that hindered their ability to address patients’ mental health, such as a lack of training and patient discomfort towards mental health work. There were also challenges that patients faced in addressing their mental health, such as a lack of relevant mental health resources, and pressures from the COVID-19 pandemic. Despite not detecting a quantifiable effect on outcomes, patients reported that working with a CHN benefited their mental health, and improved well-being and feelings of being cared for. Conclusion: The degree of consistency between the qualitative and quantitative results was mixed. The combination of challenges faced by CHNs and patients might explain why CHN support did not have a significant impact on anxiety and depression symptoms scores. However, patient reports of benefit to mental wellbeing and psychosocial health indicate that the CHN program may improve mental health in ways that were not objectively measured. This study evaluated the impact that CHNs had on anxiety and depression, and contributes to the growing knowledge on the impact of patient navigation, and the mental health of patients with CHCs in Canada.
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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 | 0.079 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".