An analysis of productivity app strengths: An environmental scan
Bibliographic record
Abstract
BACKGROUNDIn recent years, productivity apps have become the most commonly used apps. While some may feel productivity apps work solely as a result of the placebo effect , productivity apps have been shown to have three major benefits: accountability, assessment, and improvement. Stress levels were especially reduced in the two meditation groups as opposed to the muscle relaxation group . Furthermore, there has been reported evidence on the effectiveness of app-based meditation and mindfulness in reducing stress and increasing productivity for users.METHODSIn order to answer the research question, published articles from ProQuest, Business Source Premier and Web of Science were used. Additionally, Harvard Business Review was also used as a source of grey literature. Information was collected in order to determine the strengths of competing productivity apps and how they could limit or be applied to the Felicity App. The research was reviewed based on screening tools that assessed validity and relevance. Included studies were published within Asia, North America, Australia, or Europe, and were either quantitative, qualitative, randomized controlled trial (RCT), surveys, experiments with participants, or academic studies. Study screening and extraction were completed independently among two authors. Disagreements following reconciliation between the two authors were settled by a third author.RESULTSThe selected articles discuss information including methods of improving productivity, mobile-based interventions that are effective at improving either overall health or productivity, and techniques that may be applicable to the Felicity App as a mobile-based intervention.CONCLUSIONThe results show that the Felicity App can improve user outcomes by integrating features from other applications.
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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 | 0.034 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".