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Record W4402529707 · doi:10.2196/55562

Reducing the Number of Intrusive Memories of Work-Related Traumatic Events in Frontline Health Care Staff During the COVID-19 Pandemic: Case Series

2024· article· en· W4402529707 on OpenAlexvenueno aff
Veronika Kubickova, Craig Steel, Michelle L. Moulds, Marie Kanstrup, Sally Beer, Melanie Darwent, Liza Keating, Emily A. Holmes, Lalitha Iyadurai

Bibliographic record

VenueJMIR Human Factors · 2024
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
FundersVetenskapsrådetUniversity of OxfordAFA FörsäkringWellcome Trust
KeywordsIntervention (counseling)Psychological interventionDistressMental healthAnxietyMedicineHealth careBrief interventionPandemicPsychologyNursingPsychiatryClinical psychologyCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

BACKGROUND: Frontline health care staff are frequently exposed to traumatic events as part of their work. Although this study commenced before the emergence of COVID-19, levels of exposure were heightened by the pandemic. Many health care staff members report intrusive memories of such events, which can elicit distress, affect functioning, and be associated with posttraumatic stress disorder symptoms in the long term. We need evidence-based interventions that are brief, preventative, nonstigmatizing, suitable for the working lives of frontline health care staff, and effective for repeated trauma exposure. A brief, guided imagery-competing task intervention involving a trauma reminder cue and Tetris gameplay may hold promise in this regard, given evidence that it can prevent and reduce the number of intrusive memories following trauma across various settings. OBJECTIVE: This case series aims to investigate the impact of a brief imagery-competing task intervention on the number of intrusive memories, general functioning, and symptoms of posttraumatic stress, anxiety, and depression, and examine the feasibility and acceptability of the intervention for UK National Health Service frontline health care staff. The intervention was delivered with guidance from a clinical psychologist. METHODS: We recruited 12 clinical staff from the UK National Health Service, specifically from emergency departments, the intensive care unit, and the ambulance service. We evaluated the intervention using an AB single-case experimental design, where the baseline (A) was the monitoring-only phase and the postintervention (B) period was the time after the intervention was first administered. Methods were adapted once the COVID-19 pandemic began. RESULTS: There was a decrease (59%) in the mean number of intrusive memories per day from baseline (mean 1.29, SD 0.94) to postintervention (mean 0.54, SD 0.51). There was a statistically significant reduction in the number of intrusive memories from baseline to postintervention, as shown by an aggregated omnibus analysis with a small effect size (τ-U=-0.38; P<.001). Depression, anxiety, and posttraumatic stress symptoms all significantly reduced from preintervention to postintervention. Participants also reported improvements in functioning based on both quantitative and qualitative measures. The intervention was feasible to deliver and rated as acceptable by participants. CONCLUSIONS: These preliminary findings suggest that this brief therapist-guided imagery-competing task intervention offers a potential approach to mitigating the impact of work-related traumatic events in frontline health care staff, both during a pandemic and beyond. Randomized controlled trials will be an important next step.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.094
GPT teacher head0.447
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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