Rebuilding the Path of Hope —A Case Study on Anxiety Management and Psychological Support Services for Patients with Neurogenic Bladder
Bibliographic record
Abstract
A 72-year-old Chinese-Canadian male developed urination disorders due to neurogenic bladder complicated by lumbar spine surgery. After multiple treatments with poor results, he planned to undergo bladder pacemaker surgery (i.e., sacral nerve stimulation). During this period, coupled with financial pressure and the difficulty of living alone, the patient fell into a state of severe anxiety. Guided by the cognitive-behavioral theory, medical social workers conducted 8 sessions of professional interventions to help the patient reconstruct cognition, strengthen the support network, and enhance self-efficacy. Negative emotions were alleviated by adjusting irrational attribution and carrying out meditation training; the patient was encouraged to strengthen communication with his son living overseas to obtain emotional support; meanwhile, anxiety was relieved throughout the pre-operative period, and mutual assistance resources among patients were connected. In addition, medical social workers collaborated with the medical team to optimize the treatment plan, assisted in dealing with post-operative complications, and improved the patient’s treatment compliance. After the intervention, the total score of the patient’s Hospital Anxiety and Depression Scale (HAD) decreased from 28 to 13, his sleep quality was significantly improved, and he took the initiative to establish a patient support network and re-established the belief of “active coping”. This case highlights the multi-dimensional role of medical social workers in chronic disease management. Through cognitive intervention and resource integration, a path of hope for the patient’s physical and mental recovery has been built.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".