Social exclusion and Resilience: Newer Targets for Intervention
Bibliographic record
Abstract
Targets of therapeutic intervention for prevention of repeated hospitalization: need for developing ‘personalised care model’ Amresh Srivastava1, Coralee Belmont 2, Miky Kaushal 3, Avinash DeSouza 4 Robbie Campbell 5 and Larry Stitt 6 1. Associate Professor of Psychiatry, The Western University, Associate Scientist, and Lawson Health research Institute. Consultant psychiatrist Adult Ambulatory and Psychosis Program. Parkwood Institute Wellington Road. London. ON, N6C 0A7 2. Psychiatric Social worker southwest forensic mental health. St. Thomas 3. Research Fellow, Regional mental health St. Thomas 4. Research Fellow, LTMG Medical College and mental Health Resource Foundation, Mumbai, India 5. Emeritus Professor of Psychiatry. The Schulich School of medicine and dentistry. 6. Consultant, Statistical services, London. ON Canada Background Strategies to prevent repeated hospitalization generally involves implementation of multidisciplinary and enhances level of care consisting of but not limited to pharmacological, psychosocial and continuity-based interventions Despite modern, evidence-based and multifactorial/ multidimensional treatment, rates of rehospitalisation continue to rise (approximately 30-40% in 12 months) posing serious challenges for improving outcomes. A closer look at these strategies suggests that their treatments are mostly generalised in nature which lack specificity for individual patients. Seldom patient’s personal and basic characteristic which represents their ‘own-self’ as a human being is considered in clinical management protocols. These positive psychological characteristics determine the extent of psychopathology, and allow patients to build capacity to deal with it. However, meteors of assessment of such outcome indicators receive less attention. We believe that identifying indicators for patient’s ability and resources to deal with psychopathology may improve therapeutic outcomes. Knowledge about such indicators may offer better ‘personalised – care’ to minimise symptom severity Methods In this prospective study, conducted at Regional Mental Health Care ( Presently Parkwood Institute) and attending the outpatient facility were examined, using standard psychometric tools on parameters of the clinical, psychopathological, and behavioural characteristic which are related to repeated hospitalisations. Result: We assessed 101 subjects (51 females) with the mean age of 42 years. 45% were hospitalized more than once; a mean number of hospitalization was 6 and duration of illness 5.4 yrs. We found that overall psychopathology of psychosis, depression, suicidality; stressful life events and demographic factors did not differ amongst patients with one and more than one hospitalization Certain features of depression (on HDRS), suicidality, (on SISMAP), resilience, (on CD-RISC) and psychosis (on BPRS) significantly differentiated the patients who were hospitalised only one and those who were hospitalized for more than one time. More number of patients who were hospitalised only once during the similar duration of illness exhibited significantly robust characteristics of resilience on the parameters of ‘not giving up (21 (48.8%) vs 7 (21.9%) p=0.017), staying focussed under stress’, (15 (34.9%) vs.4 (12.5%). P= 0.028), having a strong sense of purpose (10 (23.3%) Vs 1 (3.1%), p= 0.020), taking pride in their achievement s (30 (69.8%) 10 (23.3%) Vs 1 (3.1%), p= 0.020 ) and having strong ability to control thoughts of self-harm (19 (79.2%) Vs 9 (45.0%) p 0.019). Over all significant number of patients with single hospitalization had the ability to adapt –to-stress, and attitude of positivity. Conclusion A number of patients with characteristics suggestive of resilience and positivity were admitted only once. These factors are modifiable by using resilience-building measures. Applying treatments to build upon such features may offer significant benefit for the treatment.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".