Risk factors for sleep disturbance and its effect on quality of life: an analysis of retrospective cohort study of adults with neck injuries
Bibliographic record
Abstract
Persons with neck injuries often have lasting psychosocial effects. One symptom that occurs in a significant number of persons is problems with sleep. Until recently, it was unclear about the predictors of sleep disturbances after a neck injury that does not tend to resolve within six months post injury. In addition, very little is known about how sleep affects overall self reported quality of life, controlling for other relevant predictors. The study aims to examine predictors of sleep disturbances after a neck injury that does not tend to resolve within six months of post injury and how sleep disturbance affects overall quality of life to those having six months post injuries. \n \nMethods \nData from a retrospective cohort study of adults have been used to investigate the study. The study population were 258 adults aged (at least 14 years) suffering from moderate to severe neck injury at an acute care Hospital in Toronto, Canada. Both exploratory as well as advanced logistic regression analyses were used in the study. \n \nResults \nThe study shows that participants who had alcohol problem are experiencing about two times higher sleep disturbance and participants with lower education are approximately 3 times more likely of having trouble with sleep than those with higher education. The injury severity and marital status are also found to be important determinant for sleep disturbances. Again, as expected, sleep disturbance and mental health are associated with defining quality of life. \n \nConclusion \nThis study reveals that marriage, alcohol problem, education, mental health and injury severity are significantly associated with sleep disturbance. On the other hand, strong associations are observed among sleep, mental health and quality of life. These finding helps in understanding risk factors related to sleep disturbance and their consequences on the quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".