What Are Young Adults Saying About Mental Health? An Analysis of Internet Blogs
Bibliographic record
Abstract
BACKGROUND: Despite the high prevalence of mental health concerns, few young adults access treatment. While much research has focused on understanding the barriers to service access, few studies have explored unbiased accounts of the experiences of young adults with mental health concerns. It is through hearing these experiences and gaining an in-depth understanding of what is being said by young adults that improvements can be made to interventions focused on increasing access to care. OBJECTIVE: To move beyond past research by using an innovative qualitative research method of analyzing the blogs of young adults (18-25 years of age) with mental health concerns to understand their experiences. METHODS: We used an enhanced Internet search vehicle, DEVONagent, to extract Internet blogs using primary keywords related to mental health. Blogs (N = 8) were selected based on age of authors (18-25 years), gender, relevance to mental health, and recency of the entries. Blogs excerpts were analyzed using a combination of grounded theory and consensual qualitative research methods. RESULTS: Two core categories emerged from the qualitative analysis of the bloggers accounts: I am powerless (intrapersonal) and I am utterly alone (interpersonal). Overall, the young adult bloggers expressed significant feelings of powerlessness as a result of their mental health concerns and simultaneously felt a profound sense of loneliness, alienation, and lack of connection with others. CONCLUSIONS: The present study suggests that one reason young adults do not seek care might be that they view the mental health system negatively and feel disconnected from these services. To decrease young adults' sense of powerlessness and isolation, efforts should focus on creating and developing resources and services that allow young adults to feel connected and empowered. Through an understanding of the experiences of young adults with mental health problems, and their experiences of and attitudes toward receiving care, we provide some recommendations for improving receptivity and knowledge of mental health care services.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".