Goal Setting and Treatment Adherence Among Patients With Chronic Illness and Depressive Symptoms: Applying a Patient-Centered Approach
Bibliographic record
Abstract
OBJECTIVE: Poor treatment adherence is a major problem among individuals with chronic illness. Research indicates that adherence is worsened when accompanied by depressive symptoms. In this preliminary study, we aimed to describe how a patient-centered approach could be employed to aid patients with depressive symptoms in following their treatment regimens. METHODS: The sample consisted of 14 patients undergoing antiretroviral therapy (ART) for HIV who reported clinically-significant depressive symptoms. Participant ratings of 23 treatment-related statements were examined using two assessment and analytic techniques. Interviews were conducted with participants to determine their views of information based on the technique. RESULTS: Results indicate that while participants with optimal adherence focused on views of treatment associated with side effects to a greater extent than participants with poor adherence, they tended to relate these side effects to sources of intrinsic motivation. CONCLUSION: The study provides examples of how practitioners could employ the assessment techniques outlined to better understand how patients think about treatment and aid them in effectively framing their health-related goals.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".