Patient-perceived barriers to a screening program for depression: a patient opinion survey of hemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Depression is a prevalent, yet underdiagnosed, psychiatric disorder among patients with end-stage renal disease. Active case identification through routine screening is suggested; however, patient-related barriers may reduce the effectiveness of screening for, and treating, depression. This study aimed to explore the perceived barriers that limit patients from participating in screening and treatment programs for depression. METHODS: The two-item Patient Health Questionnaire was used to identify patients with depressive symptoms. RESULTS: Of 160 participants, 73.1% reported at least one barrier preventing them from participation [95% confidence interval (95% CI) 66.2-80.0%]. Patients with depressive symptoms were more likely to perceive at least one barrier to a screening program for depression compared with those without depressive symptoms (96% versus 68.9%, respectively; odds ratio = 10.8; 95% CI 1.4-82.8; P = 0.005). The association of the barrier scores with depressive symptoms remained significant after adjustment for patient's characteristics. The most common barriers that patients expressed were concerns about the side effects of any antidepressant medications that may be prescribed (40%), concerns about having more medications (32%), feeling that the problem is not severe enough (23%) and perceiving no risk of depression (23%). CONCLUSIONS: Negative perceptions about depression and its treatment among hemodialysis patients constitute an important barrier to identifying this condition and first need to be addressed before implementing a screening program in this population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".