The Q-Life experience: An evaluation of an employee resilience program
Bibliographic record
Abstract
BACKGROUND: Many employers are turning to training programs to help them cope or flourish in life. Many programs on the market have not been validated. OBJECTIVE: The objective was to evaluate the effect building awareness and skill development can have on sustaining high resilience within a group of employees. METHODS: 524 participants completed the 44 itemed Q-Life assessment. A CFA model was conducted to determine whether the Q-Life score, adequately describes the data. 116 employees signed up to the Q-Life experience with 64 completing all requirements. RESULTS: The RMSEA index was less than 0.08, RMSEA = 0.07, 90%CI = [0.07, 0.07], which is indicative of a good model fit. The mean of score for resilience on the first test (M = 249.91) was significantly lower than the mean of post-test (M = 264.91). CONCLUSIONS: The results indicated that the model demonstrated acceptable fit to the data and can be used as an assessment tool for the Q-Life. The Q-Life experience showed significant improvement in resilience.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".