Can self-esteem be improved using short daily training on mobile applications? Examining real world data of GG Self-esteem users
Bibliographic record
Abstract
Objective Using real world data, we examined the associations between self‐esteem ratings and the short, daily use of a cognitive behavioural therapy (CBT)‐based mobile application targeting self‐esteem related beliefs. The effects of using this application on mood ratings were also assessed.Method Real world data of GG Self‐esteem users were collected from January 2019 until August 2019. Participants’ self‐esteem and mood scores were evaluated at three‐time points corresponding to Levels 1, 20 and 46 of the mobile application.Results Significant increases in self‐esteem ratings were found across all three‐time points. Increased mood ratings were only found at Level 20, compared to baseline. Dropout rates across assessment points were associated with younger age, and males showed significantly higher self‐esteem scores than women at baseline and the second assessment point.Conclusions Our findings are consistent with previous controlled trials indicating that using CBT‐based mHealth applications targeting maladaptive beliefs may be useful for increasing user's wellness and reducing distress.KEY POINTS(1) App use was associated with increased self‐esteem and mood.(2) The higher the level participants used the app the more their self‐esteem increased.(3) Real world data is consistent with findings from previous controlled studies.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".