Effective Virtual Interventions to Enhance Psychological Capital: A mixed-methods systematic review
Bibliographic record
Abstract
BACKGROUNDDeveloped from positive psychology, Psychological Capital (PsyCap) entails a collection of intrinsic traits which may be optimized for productive and sustainable outcomes in life. This systematic review explores potential virtual implementation of PsyCap interventions, especially given the digital transition amidst the COVID-19 pandemic and its potential usage in the future.METHODSUtilizing a mixed-methods systematic review, this convergent integrated synthesis involves database searches conducted in APA PsychINFO, Web of Science and PubMed with literature published between 1995 and 2020. This systematic review follows the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines with a registered PROSPERO protocol. With diverse study designs and accompanying grey literature, heterogeneity precluded statistical analysis for qualitative presentation of included studies. Study screening, extraction, and quality appraisal (using the Mixed Methods Appraisal Tool) were performed by two authors independently and reconciled. Disagreements were resolved by a third author.RESULTSPresent literature has determined effective increase of PsyCap with the PCI Intervention Model. Strengths-based interventions assisted in identifying individual recognition in strengths to maximize potential and increase PsyCap. Other interventions have been found to support hope, self-efficacy, resilience, or optimism (HERO).CONCLUSIONOverall, interventions from included studies showed effective improvement in HERO elements and increased PsyCap in individuals in academia and the workplace. In the context of the COVID-19 pandemic and future application, PsyCap interventions may be further explored and modified for virtual implementation for young adults.
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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 | 0.035 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".