Awareness, Acceptability, and Perceived Effectiveness of Text-Based Therapy Among Graduate Students: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Research has suggested that there is a mental health crisis occurring among graduate students in the United States. Moreover, many students go without effective treatment owing to the limited availability of mental and behavioral health resources on college campuses. Text-based therapy may represent a viable method for increasing access to mental health support for graduate students, but little is known regarding its acceptability in this population. OBJECTIVE: The purpose of this study was to assess how graduate students perceive text-based therapy and their likelihood of seeking out this form of therapy. METHODS: In total, 265 graduate students completed a cross-sectional web-based survey that included multiple-choice and open-ended questions assessing their perceptions of text-based therapy and the likelihood of seeking out this form of therapy. Chi-square tests, ANOVAs, and nonparametric Wilcoxon signed-rank tests were used to examine differences in multiple-choice questions. The constant comparative method was used for qualitative analyses of the open-ended question responses. RESULTS: Participants (n=265) were predominately non-Hispanic White (166/265, 62.6%) and female (167/265, 63%) with a mean age of 28.3 (SD 5.1) years. Over half of the participants (139/265, 52.5%) were not aware that text-based therapy existed; however, 65.3% (173/265) reported that they would consider using text-based services, if available. In comparison to face-to-face therapy, participants reported being less likely to seek out text-based therapy and perceived it as less effective (P<.001). Qualitative results indicated that participants were concerned about the ability to effectively communicate and build rapport through text-based therapy and thought that this modality may be more effective for some mental and behavioral health concerns than others. Moreover, participants noted that text-based therapy would be best implemented as a way to supplement, rather than replace, face-to-face services. CONCLUSIONS: Altogether, the results of this study suggest that text-based therapy holds the potential to increase access to and use of mental and behavioral health services; however, graduate students remain concerned about its effectiveness and the optimal methods of implementation. Future research should investigate how therapeutic processes (eg, effective communication and rapport-building) can be facilitated in digital environments and how text-based therapy could be best implemented to supplement and extend, rather than replace, face-to-face 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".