Scoping review on productivity-related psychological interventions: Integration into the Felicity App
Bibliographic record
Abstract
BACKGROUND: The Felicity App is a mobile application undergoing development with the aim to integrate practical psychological interventions with recreational elements to reinforce positive and productive behaviours. Prior to construction, psychological interventions must be reviewed for relevance, effectiveness and ability to be integrated within Felicity. Psychological concepts are reviewed as the preliminary phase prior to further investigation in subfields. METHODS: Electronic databases were searched including (and not limited to) PsychInfo, PubMed, Web of Science, and ScienceDirect from November 29, 2020 to December 5, 2020. Further filtering methods were applied accordingly, and grey literature was excluded. Common and effective psychological interventions related to productivity were screened and extracted, with an emphasis on the reinforcement and discouragement of productivity-related behaviours. RESULTS: Productivity-related psychological interventions were addressed according to three categories: 1) Motivation 2) Procrastination 3) Time management. Within motivation, self-affirmation is an effective method, particularly when accompanied with other positive thinking techniques. Intrinsic motivation could be enhanced with goals aligned in specific criterias. Additionally, virtual rewards, engagement, and familiarity serve as vital components to enhance motivation. Procrastination is associated with lower engagement in work and interventions include various therapy measures. Within time management, clear purpose has been shown as an effective tool for goal setting while strategies include spaced learning and other study techniques. CONCLUSION: This scoping review determined several points of connection between motivation, procrastination, and time management with many effective interventions which could be potentially integrated within the Felicity App for personal development and habit-building. Additional research should be conducted to further determine the effectiveness of interventions for integration into Felicity.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".