Different communication strategies for disclosing a diagnosis of schizophrenia and related disorders
Notice bibliographique
Résumé
BACKGROUND: Delivering the diagnosis of a serious illness is an important skill in most fields of medicine, including mental health. Research has found that communication skills can impact on a person's recall and understanding of the diagnosis, treatment options and prognosis. People may feel confused and perplexed when information about their illness is not communicated properly. Sharing information about diagnosis of a serious mental illness is particularly challenging. The nature of mental illness is often difficult to explain since there may be no clear aetiology, and the treatment options and prognosis may vary enormously. In addition, newly diagnosed psychiatric patients, who are actively ill, often may not accept their diagnosis due to lack of insight or stigma attached to the condition. There are several interventions that aim to help clinicians to communicate life changing medical diagnoses to people; however, little is known specifically for delivering a diagnosis of schizophrenia. OBJECTIVES: To evaluate evidence from randomised controlled trials (RCTs) for the efficacy of different communication strategies used by clinicians to inform people about the diagnosis and outcome of schizophrenia compared with treatment as usual and to compare efficacy between different communication strategies. SEARCH METHODS: On 22 June 2015 and 29 June 2016, we searched the Cochrane Schizophrenia Group's Study-Based Register of Trials. We also searched sources of grey literature (e.g., dissertations, theses, clinical reports, evaluations published on websites, clinical guidelines and reports from regulatory agencies). SELECTION CRITERIA: We planned to include all relevant RCTs that included adults with schizophrenia or related disorders, including schizophreniform disorder, schizoaffective disorder and delusional disorder. The trials would have investigated the effects of communication strategy or strategies that helped clinicians deliver information specifically about a diagnosis of schizophrenia (which can also include communication regarding the treatment options available and prognosis). DATA COLLECTION AND ANALYSIS: Review authors independently examined all reports from the searches for any relevant studies. We planned to extract data independently. For binary outcomes, we would have calculated risk ratio (RR) and its 95% confidence interval (CI), on an intention-to-treat basis. For continuous data, we would have estimated the mean difference (MD) between groups and its 95% CI. We would have employed a random-effects model for analyses. We planned to assess risk of bias for included studies. We created a 'Summary of findings' table using GRADE. MAIN RESULTS: The searches identified 44 records which appeared to be relevant to the aims of the review. We obtained full reports for seven potential studies; however, after close inspection none of these studies met the inclusion criteria. AUTHORS' CONCLUSIONS: Good communication of diagnosis can affect treatment planning, compliance and patient outcomes, especially in the case of conditions such as schizophrenia, which has the potential to cause serious life disruption for both people with schizophrenia and their carers. Currently, there is no evidence based on findings from RCTs assessing the effects of communication strategies for disclosing the diagnosis of schizophrenia and related disorders. Research is required.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,016 | 0,069 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,007 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 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 ».