Integrating general practitioners into crisis management would accelerate the transition from victim to effective professional: Qualitative analyses of a terrorist attack and catastrophic flooding
Bibliographic record
Abstract
BACKGROUND: In 2018, Trèbes, 6,000 inhabitants with nine general practitioners (GPs) in southern France, experienced two tragedies; a terrorist attack in March, in which four people were killed, and a catastrophic flood in October, in which six people died and thousands more were affected. OBJECTIVES: We aimed to obtain a substantive theory for improving crisis management by understanding the personal and professional effects of the two successive disasters on GPs in the same village. METHODS: This qualitative study conducted complete interviews with eight GPs individually, with subsequent analyses involving the conceptualisation of categories based on grounded theory. RESULTS: The analysis revealed that GPs underwent a double status transition. First, doctors who experienced the same emotional shock as the population became victims; their usual professional relationship changed from empathy to sympathy. The helplessness they felt was amplified by the lack of demand from the state to participate in the first emergency measures; consequently, they lost their professional status. In a second phase, GPs regained their values and skills and acquired new ones, thus regaining their status as competent professionals. In this context, the participants proposed integrating a coordinated crisis management system and the systematic development of peer support. CONCLUSION: We obtained valuable information on the stages of trauma experienced by GPs, allowing a better understanding of the effects on personal/professional status. Thus, the inclusion of GPs in adaptive crisis management plans would limit the effects of traumatic dissociation while increasing their professional 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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".