Interventional procrastination strategies for young adults in virtual settings: A systematic review
Bibliographic record
Abstract
BACKGROUNDThe phenomenon of academic procrastination affects nearly 95% of post-secondary students within academia with its association seen in reduced academic performance. Amidst the onset of COVID-19, this systematic review aims to provide insight towards potential virtual interventions to support personal management of academic procrastination for young adults. METHODSThe selection procedure followed the Preferred Reporting Items for Systematic Review and MetaAnalyses (PRISMA) guidelines. The protocol was registered with PROSPERO (record ID: CRD42021234243). Studies were reviewed based on a pre-determined and piloted screening tool with study screening and extraction performed independently among two authors. After reconciliation, disagreements were settled by a third author. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT) in duplicate. Heterogeneity in study designs, outcomes, and measurements precluded meta and statistical analyses; thus, a qualitative analysis of studies was provided. RESULTSA total of 49 studies were included with identification of two primary web-based interventions for reducing academic procrastination in a mobile app setting. The first intervention involves emotional management through increasing tolerance and modifying negative emotions which determines the activity with the highest likelihood of procrastination each day, and the subject's resilience or the subject's commitment should be emphasized. The second intervention involves mental imagery for subjects to feel an affinity for his/her future self, and thus decrease procrastination behaviours. CONCLUSIONTwo main virtual interventions were determined from the systematic review: (1) an intervention for managing personal emotions, and (2) an intervention that involves creating a mental image of one's future self.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".