Different communication strategies for disclosing a diagnosis of schizophrenia and related disorders
Bibliographic record
Abstract
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.
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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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".