Examining the experiences of pediatric mental health care providers during the early stage of the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic fundamentally impacted the way that mental health services were provided. In order to prevent the spread of infection, many new public health precautions, including mandated use of masks, quarantine and isolation, and closures of many in-person activities, were implemented. Public health mandates made it necessary for mental health services to immediately shift their mode of delivery, creating increased confusion and stress for mental health providers. The objective of this study is to understand the impact of pandemics on the clinical and personal lives of mental health providers working with children during the early months of the COVID-19 pandemic, March -June 2020. METHODS: Mental health providers (n = 98) were recruited using purposive sampling from a public health service in Canada. Using qualitative methods, semi-structured focus groups were conducted to understand the experiences of mental health service providers during the beginning of the COVID-19 pandemic. RESULTS: Data from the focus groups were analysed and three main themes emerged: (1) shift to virtual delivery and working from home; (2) concerns about working in person; (3) exhaustion and stress from working through the pandemic. DISCUSSION: This study gave voice to mental health providers as they provided continuity of care throughout the uncertain early months of the pandemic. The results provide insight into the impact times of crisis have on mental health providers, as well as provide practical considerations for the future in terms of supervision and feedback mechanisms to validate experiences.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".