Did the COVID-19 pandemic cause the predicted “tsunami” of mental health crises?
Bibliographic record
Abstract
Background Second victims are healthcare workers who experience emotional distress following patient adverse events. Studies indicate the need to develop organisational support programmes for these workers. The RISE (Resilience In Stressful Events) programme was developed at the Johns Hopkins Hospital to provide this support. Objective To describe the development of RISE and evaluate its initial feasibility and subsequent implementation. Programme phases included (1) developing the RISE programme, (2) recruiting and training peer responders, (3) pilot launch in the Department of Paediatrics and (4) hospital-wide implementation. Methods Mixed-methods study, including frequency counts of encounters, staff surveys and evaluations by RISE peer responders. Descriptive statistics were used to summarise demographic characteristics and proportions of responses to categorical, Likert and ordinal scales. Qualitative analysis and coding were used to analyse open-ended responses from questionnaires and focus groups. Results A baseline staff survey found that most staff had experienced an unanticipated adverse event, and most would prefer peer support. A total of 119 calls, involving ∼500 individuals, were received in the first 52 months. The majority of calls were from nurses, and very few were related to medical errors (4%). Peer responders reported that the encounters were successful in 88% of cases and 83.3% reported meeting the caller9s needs. Low awareness of the programme was a barrier to hospital-wide expansion. However, over the 4 years, the rate of calls increased from ∼1–4 calls per month. The programme evolved to accommodate requests for group support. Conclusions Hospital staff identified the need for a multidisciplinary peer support programme for second victims. Peer responders reported success in responding to calls, the majority of which were for adverse events rather than for medical errors. The low initial volume of calls emphasises the importance of promoting awareness of the value of emotional support and the availability of the programme.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".