Social exclusion and Resilience: Newer Targets for Intervention
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».