Psychotherapy via Telehealth during the COVID-19 Pandemic in Australia–Experience of Clients with a Diagnosis of Borderline Personality Disorder
Bibliographic record
Abstract
OBJECTIVE: To investigate the transition to, and experience of, telehealth in people with a diagnosis of borderline personality disorder (BPD). METHOD: A cross-sectional study using an online survey was conducted in a specialist clinic for personality disorders in March-May 2020. RESULTS: Thirty-seven clients (48% response rate) completed the survey. Two participants (5.4%) were decided not to receive treatment via telehealth. Transitioning from in-person to telehealth, the majority of participants had few or no technical issues (51.4%). Telephone, video-conferencing and a mix of telephone and video-conferencing were used. Positive and negative experiences were endorsed asking about the effectiveness of telehealth. While some participants were whether unsure (32%) or not (19%) interested in telehealth following pandemic, half acknowledged the presence of telehealth (54.8%) and wanted to have the option of telehealth following pandemic (48.6%). CONCLUSIONS: Despite some shortcomings associated with telehealth, almost every client continued to attend appointments and half of the study participants wanted to have the option of telehealth in the future. Healthcare policymakers and mental health managers should consider the challenges described in this study while developing telehealth guidelines to best support people experiencing problems living with the psychiatric diagnoses of BPD.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".