The effects of technology on stress and coping strategies in nurse educators
Bibliographic record
Abstract
The purpose of this study was to investigate the relationships among the independent variables of age, gender, years of experience as a nurse educator, and previous technology training, and the dependent variables of feeling compelled to respond to students after hours, level of stress experienced by nurse educators with technology (in general), and level of stress experienced by nurse educators with technology in the classroom/clinical setting. The researcher also investigated the coping strategies demonstrated by these nurse educators. The target population was defined as master’s prepared nurse educators in a nursing program who utilize technology while teaching a nursing theory or clinical course. Of the thirty-six inquiries sent, twenty-two subjects participated in the voluntary survey, resulting in a 61% response rate. Overall, the independent variables were found to not be significantly associated with the measure of the dependent variable of overwhelming feelings of stress or anxiety related to technology. For the measure of the dependent variable of “feeling compelled to answer emails/texts after hours”, age was the only significant predictor. It is now ever more important for nursing faculty to engage in life-long learning in informatics. Deans need to support IT initiatives, and ensure that all faculty members have competency in computer literacy during the interview process.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".