Meaningful Work, Employee Engagement, and Other Key Employee Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Problem. Meaningful work is underrepresented in current models and measures of work characteristics. Ironically, past research suggests that meaningful work may have substantive impacts on employee outcomes. The current study addresses this problem by demonstrating the value of meaningful work in human resource development (HRD) practices involving employee engagement. The Solution. A web-based survey of employed North Americans ( n = 574) was conducted. Meaningful work characteristics were compared to other work characteristics as correlates and predictors of employee engagement, burnout, job satisfaction, organizational commitment, and turnover cognitions. Meaningful work characteristics had the strongest relative correlations with multiple employee outcomes. They also predicted substantive variance in employee engagement while controlling for other work characteristics in regression analyses. The Stakeholders. Since meaningful work contains themes of human development (e.g., self-actualization, social impact), this variable represents an opportunity for human resource development (HRD) practitioners to increase levels of employee engagement as a strategic leverage point within organizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it