The Distress Management System for Stroke (DMSS): An approach for screening and initial intervention for post-stroke psychological distress
Bibliographic record
Abstract
Aim: To develop a practical system to enable nurses to screen for psychological distress following stroke. Background: Mood disorders and psychological distress are prevalent following stroke, but often go undetected and therefore untreated. National guidelines recommend screening for distress, but the evidence suggests that there are significant barriers to the implementation of post-stroke mood and psychological distress screening programmes. One barrier relates to limitations of questionnaire methods for identifying distress. Specifically, existing mood questionnaires are often too lengthy; miss important emotional changes that result from stroke; and do not necessarily point nurses towards the most helpful intervention approaches. Methods: We sought to develop a brief, clinically valid screening system that could be used by nurses to identify the levels of psychological distress experienced by stroke survivors. As well as determining levels of distress, the system was intended to aid the identification of first-line interventions that nurses could offer to alleviate distress. Results: A screening system was identified from the oncology literature (the Distress Management System [DMS]) that appeared to meet the broad requirements for screening for stroke-related distress. To tailor the DMS for stroke settings, we obtained feedback on the components of the DMS from stroke survivors and stroke nurses, and also consulted the research literature. This led us to: (1) retain the distress screening component of the DMS, the ‘Distress Thermometer’; (2) modify the DMS’s ‘Concerns Checklist’ to reflect the main problems encountered after stroke; and (3) add a ‘Resource Pack’ to the screening system to enable nurses to offer first-line interventions targeted at individuals’ distress. The result, the Distress Management System for Stroke (DMSS), is presented in full in this paper. Conclusion: The DMSS is a potentially useful tool for nurses working in stroke settings. It is brief, straightforward to administer, captures the main emotional concerns of stroke survivors, and the findings from the DMSS can be used to structure interventions for psychological distress. Future work is required, however, to establish the DMSS’s reliability, validity and clinical effectiveness.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".