Mental health sequelae in the perioperative period: An examination of perioperative dissociation, postoperative delirium, and posttraumatic stress
Bibliographic record
Abstract
Perioperative dissociation and postoperative delirium are adverse mental health outcomes that can occur in the context of surgery and are both independently associated with posttraumatic stress (PTS). Using quantitative and qualitative methodologies across two studies, this thesis aimed to identify whether symptom overlap exists between perioperative dissociation and postoperative delirium, examine how perioperative dissociation and postoperative delirium are associated with PTS (study 1; quantitative), understand how individuals describe and experience symptoms of dissociation and delirium, and examine the impact of these symptoms on the recovery process (study 2; qualitative). Both studies included a sub-sample of non-emergent surgical patients (study 1: n = 181; study 2: n = 5), aged 60 years and older, who participated in the Electroencephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes (ENGAGES-CANADA; N = 492 at time of analysis) study. In study 1, a principal components analysis did not support symptom overlap between dissociation and delirium and multiple linear regressions revealed both symptom presentations were associated with PTS; however, differential relationships emerged according to the covariates included. Using qualitative methods informed by constructivist grounded theory, study 2 revealed a main theme of silence of perioperative mental health; participants did not describe or elaborate on perioperative mental health experiences when asked, and focused on their physical health-related experiences. Findings of these two studies highlight important similarities and differences between dissociation and delirium. In addition, results underscore the importance of communication, assessment, and monitoring of these symptoms, which may help reduce adverse perioperative mental health outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".