“I could hang up if the practitioner was a prat”: Australian men’s feedback on telemental healthcare during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic restrictions, uncertainties and management inconsistencies have been implicated in men's rising distress levels, which in turn have somewhat normed the uptake of telemental healthcare services (i.e., phone and/or video-conference-based therapy). Given past evidence of poor engagement with telemental health among men, this mixed-methods study examined Australian men's use of, and experiences with telemental health services relative to face-to-face care during the pandemic. A community sample of Australian-based men (N = 387; age M = 47.5 years, SD = 15.0 years) were recruited via Facebook advertising, and completed an online survey comprising quantitative items and open-response qualitative questions with the aim of better understanding men's experiences with telemental healthcare services. In total, 62.3% (n = 241) of participants reported experience with telemental health, and regression analyses revealed those who engaged with telemental health were on average younger, more likely to be gay and university educated. Men who had used telemental health were, on average, more satisfied with their therapy experience than those who had face-to-face therapy. Among those who had telemental healthcare, marginally lower satisfaction was observed among regional/rural based relative to urban men, and those who had to wait longer than 2 months to commence therapy. Qualitative findings highlighted positive aspects of telemental healthcare including comfort with accessing therapy from familiar home environments and the convenience and accessibility of telemental health alongside competing commitments and COVID-19 restrictions. Conversely, drawbacks included technical limitations such as crosstalk impeding therapeutic progress, disconnects and audio-visual lag-times and the 'impersonal' nature of telemental healthcare services. Findings broadly signal COVID-19 induced shifts norming of the use of virtual therapy services, with clear scope for improvement in the delivery of therapeutic practice using digital modalities, especially among help-seeking men.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".