Applying the Theory of Planned Behavior to Correct Posture in Operating Room Staffs
Bibliographic record
Abstract
OBJECTIVE: Ergonomic risk factors such as prolonged and awkward postures increase the risk of work related musculoskeletal disorders (WRMSDs) in operating room staffs. Understanding the factors influencing the prevalence of the WRMSDs is an essential step in any targeted health promotion interventions. This research aimed to determine the factors associated with correct posture maintenance based on the theory of planned behavior (TPB) among the operating room staffs from educational hospital affiliated to Qazvin university medical sciences, in 2013. METHODS: A total of 130 subjects with mean ages of 31.2±6.38 years participated in this study. Demographic data and TPB constructs were assessed using reliable and valid scales. Path analysis, based on TPB components, was applied to determine specific factors that most contribute to and predict actual behavior toward correct posture maintenance. RESULTS: Psychometric properties of the model were consistent with the recommendations and results showed that variables were fit to the data. 58% of the variance in behavioral intention (BI) was described by the TPB constructs (P<0.05). Also, attitude (AT), subjective norms (SN), perceived behavioral control (PBC) and BI explained 39% of the variance in maintenance of a correct posture (P<0.05). Consistent with predictions from the TPB, AT (βi=0.44, P<0.05) were the major predictors of BI. In addition, PBC (βi=0.52, P<0.05) and BI (βi=0.41, P<0.05) were the important factors that influence the maintenance of a correct posture in the operating room staffs. CONCLUSION: As a conclusion, TPB is a useful model to determine and to predict maintenance of a correct posture in the operating room staffs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".