Dispositional mindfulness and its relationship to preoperative anxiety and postoperative pain in adults undergoing hysterectomy
Bibliographic record
Abstract
The purpose of this dissertation was to examine how mindfulness practices might be able to benefit surgical patients. Manuscript 1 was a critical review of psychometric questionnaires used to measure mindfulness. It updated prior critiques of mindfulness questionnaires by including newer tools and was the first updated review of this topic in about a decade. Manuscript 2 was an integrative review of where mindfulness was studied with surgical patients, specifically around the experiences of preoperative anxiety and postoperative pain during the perioperative timeframe. It is likely the most comprehensive review of perioperative mindfulness studies to date. Manuscript 3 was an examination of dispositional mindfulness and preoperative anxiety and postoperative pain in adults having gynecological surgery. It went beyond many prior studies in that it included preoperative anxiety, postoperative pain, postoperative opioid consumption, and length of stay. Collectively, this dissertation work suggests that adding a preoperative mindfulness-based intervention into surgical pathways could have some benefit for patients by reducing anxiety, pain and opioid consumption. More research needs to be done to determine the specific benefits of mindfulness-based interventions in surgical patients, including which populations might benefit most, and the most effective intervention modality, timing, and delivery methods. --Author's abstract
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".