Designing Online Interventions in Consideration of Young People’s Concepts of Well-Being: Exploratory Qualitative Study
Bibliographic record
Abstract
BACKGROUND: A key challenge in developing online well-being interventions for young people is to ensure that they are based on theory and reflect adolescent concepts of well-being. OBJECTIVE: This exploratory qualitative study aimed to understand young people's concepts of well-being in Australia. METHODS: Data were collected via workshops at five sites across rural and metropolitan sites with 37 young people from 15 to 21 years of age, inclusive. Inductive, data-driven coding was then used to analyze transcripts and artifacts (ie, written or image data). RESULTS: Young adults' conceptions of well-being were diverse, personally contextualized, and shaped by ongoing individual experiences related to physical and mental health, along with ecological accounts acknowledging the role of family, community, and social factors. Key emerging themes were (1) positive emotions and enjoyable activities, (2) physical wellness, (3) relationships and social connectedness, (4) autonomy and control, (5) goals and purpose, (6) being engaged and challenged, and (7) self-esteem and confidence. Participants had no difficulty describing actions that led to positive well-being; however, they only considered their own well-being at times of stress. CONCLUSIONS: In this study, young people appeared to think mostly about their well-being at times of stress. The challenge for online interventions is to encourage young people to monitor well-being prior to it becoming compromised. A more proactive focus that links the overall concept of well-being to everyday, concrete actions and activities young people engage in, and that encourages the creation of routine good habits, may lead to better outcomes from online well-being interventions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".