Boosting productivity and wellbeing through time management: evidence-based strategies for higher education and workforce development
Bibliographic record
Abstract
Introduction Amid increasing academic and professional pressures, time management is widely acknowledged as essential for supporting students' and professionals' well-being, motivation, and performance. However, despite general agreement on its benefits, there remains limited clarity about which specific time management strategies are most effective, particularly in the context of higher education and workforce development. Compounding this issue are inconsistencies in how time management is defined and measured across the literature. Method This systematic review synthesizes findings from 107 empirical studies—spanning higher education and workplace settings and including peer-reviewed journal articles and dissertations—to clarify the conceptual landscape of time management, identify high-impact strategies, and assess their influence on key outcomes. Following PRISMA guidelines, we conducted a comprehensive search across PsycINFO, ERIC, ProQuest Dissertations & Theses, and Google Scholar, including studies that employed quantitative, qualitative, and mixed-methods designs to ensure a broad and nuanced understanding of the topic. Results Planning, goal-setting, prioritization, and task organization emerged as particularly beneficial strategies for enhancing productivity, well-being, and overall performance. Discussion By addressing definitional inconsistencies and identifying the most effective strategies, this review offers evidence-based guidance for educators, instructional designers, and career development professionals seeking to better equip students and workers for success in an increasingly demanding and competitive environment.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".