The influence of societal behaviours and trends on mental health based applications: An environmental scan
Bibliographic record
Abstract
BACKGROUNDWithin a decade, the expeditious evolution of technology has catalyzed digital advancements employed even at one’s fingertips. Now a household item, smartphones host innumerable apps supporting users across the world. One of the rising market niches includes mental-health based applications, which further branch into topics of lifestyle, habit-building, and productivity.METHODSThis systematic review explores quantitative and qualitative studies discussing variables of influence on social behaviours and trends for mental-health based applications. Searched databases include Science Direct, ProQuest, and Scopus with further supplementation of grey literature from Science Daily, Common Sense Media, and Extreme Networks. Extracted data precluded meta-analysis given the significant heterogeneity in study design, outcomes, and measurements. Studies were screened with a piloted tool and screening & extraction was completed independently among two authors. Disagreements following reconciliation between the two authors were settled by a third author. RESULTSAppraised articles identified trends & behaviors associated with app functionality, accessibility, and data/privacy security. The decline or absence of these features correlated with low user engagement.CONCLUSIONPrevalent features were determined for enhanced functionality, accessibility, and database security, which may serve to bolster apps within mental-health and its niches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".