Change your life through journaling--The benefits of journaling for registered nurses
Bibliographic record
Abstract
Objective : The objective of this study was to determine the effect journaling had on the degree of compassion satisfaction (CS), burnout (BO), and trauma/compassion fatigue (TCF) present in registered nurses (RNs). A secondary objective of this study was to gain knowledge about participants’ experiences with journaling. Methods : This study was a pre-test, post-test quasi-experimental design with a qualitative component. A total of 66 registered nurses were recruited to participate in a journaling class. Each RN completed the Professional Quality of Life Scale Survey Revision IV (ProQOL R-IV) three times. In addition to the surveys, participants were asked to answer two open-ended questions. Results : CS, BO, and TCF all improved after taking the course. The overall change from Pre-survey to Post II-survey was statistically significant for compassion satisfaction ( p = .008); burnout ( p = .0001); and, trauma compassion fatigue ( p = .0001). During constant-comparative analysis three themes were identified as: 1) journaling allowed me to unleash my inner most feelings, 2) journaling helped me to articulate and understand my feelings concretely, and 3) journaling helped me make more reasonable decisions. Conclusions : This study provides valuable information about journaling having a positive effect over time on the ability of registered nurses to handle stress, increase CS, and decrease BO and TCF symptoms. While this information adds to the limited literature, further research needs to be conducted with a larger sample.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".