Identifying Core Competencies for Remote Delivery of Psychological Interventions: A Rapid Review
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic led to a rapid shift toward remote delivery of psychological interventions and transition to voice-only and video communication platforms. However, agreement is lacking on key competencies that are aligned with equitable approaches for standardized training and supervision of remote psychological intervention delivery. A rapid review was conducted to identify and describe competencies that could inform best practices of remote services delivery during and after the COVID-19 pandemic. METHODS: Scopus, MEDLINE, and PsycINFO were searched for literature published in English (2015-2021) on competencies for synchronous, remote psychological interventions that can be measured through observation. RESULTS: Of 135 articles identified, 12 met inclusion criteria. Studies targeted populations in high-income countries (11 in the United States and Canada, one in Saudi Arabia) and focused on specialist practitioners, professionals, or trainees in professional or prelicensure programs working with adult populations. Ten skill categories were identified: emergency and safety protocols for remote services, facilitating communication over remote platforms, remote consent procedures, technological literacy, practitioner-client identification for remote services, confidentiality during remote services, communication skills during remote services, engagement and interpersonal skills for remote services, establishing professional boundaries during remote services, and encouraging continuity of care during remote services. CONCLUSIONS: These 10 skills domains can offer a foundation for refinement of discrete, individual-level competencies that can be aligned with global initiatives promoting use of observational competency assessment during training and supervision programs for psychological interventions. More research is needed on identification of and agreement on remote competencies and on their evaluation.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".