97 COVID Check-In: Optimizing Cleft & Craniofacial Team Care during COVID-19 using an on-line social paediatrics screening instrument
Bibliographic record
Abstract
Abstract Background Public health mandates associated with COVID-19 added unprecedented stress on families, providers and the health care systems; including our ambulatory multidisciplinary cleft team care. In order to continue to provide responsive, multidisciplinary team (MDT) cleft and craniofacial care, it was essential to identify direct and indirect impacts of COVID-19 on our patients and families, whilst planning ahead for ongoing coordinated surgical, pediatric, and psychosocial interventions. Our team developed a short on-line psychosocial screening questionnaire that was administered prior to our MDT clinics, using a “What Matters to You” quality improvement (QI) format. Objectives 1.To establish and test the utility of a “What Matters to You” quality improvement (QI) questionnaire. 2.To use this questionnaire in order to understand the impact of the COVID-19 pandemic on patients and family access to health services. Design/Methods Our team developed a 5-question online smartphone-accessible survey and distributed this to families prior to their MDT clinic visit during the COVID-19 pandemic. We analyzed survey results from May 2020-October 2021 in order to understand the impact of COVID-19 on families as well as the utility of our survey system. Results Out of 110 sequential MDT visits, families reported that COVID impacted timely access to health services (20%); employment (32%); basic needs like food and shelter (13%); and social capital. Almost half (47%) reported less than 5 people to turn to for extra support. The most common concerns caregivers have about their children were development, learning, and/or school progress (38%); mental health (36%) and social emotional well-being (31%). Conclusion Our study shows that 5 key psychosocial screening questions can be utilized to facilitate care coordination, responsiveness, and triage for in-person and virtual care settings, and respond to family centred care priorities in the midst of evolving COVID-19 landscapes.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".