Translating COVID-19 Evidence to Maximize Physical Therapists’ Impact and Public Health Response
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) has sounded alarm bells throughout global health systems. As of late May, 2020, over 100,000 COVID-19-related deaths were reported in the United States, which is the highest number of any country. This article describes COVID-19 as the next historical turning point in the physical therapy profession's growth and development. The profession has had over a 100-year tradition of responding to epidemics, including poliomyelitis; 2 world wars and geographical regions experiencing conflicts and natural disasters; and, the epidemic of noncommunicable diseases (NCDs). The evidence-based role of noninvasive interventions (nonpharmacological/nonsurgical) that hallmark physical therapist practice has emerged as being highly relevant today in addressing COVID-19 in 2 primary ways. First, despite some unique features, COVID-19 presents as acute respiratory distress syndrome in its severe acute stage. Acute respiratory distress syndrome is very familiar to physical therapists in intensive care units. Body positioning and mobilization, prescribed based on comprehensive assessments/examinations, counter the negative sequelae of recumbency and bedrest; augment gas exchange and reduce airway closure, deconditioning, and critical illness complications; and maximize long-term functional outcomes. Physical therapists have an indisputable role across the contiuum of COVID-19 care. Second, over 90% of individuals who die from COVID-19 have comorbidities, most notably cardiovascular disease, hypertension, chronic lung disease, type 2 diabetes mellitus, and obesity. Physical therapists need to redouble their efforts to address NCDs by assessing patients for risk factors and manifestations and institute evidence-based health education (smoking cessation, whole-food plant-based nutrition, weight control, physical activity/exercise), and/or support patients' efforts when these are managed by other professionals. Effective health education is a core competency for addressing risk of death by COVID-19 as well as NCDs. COVID-19 is a wake-up call to the profession, an opportunity to assert its role throughout the COVID-19 care continuum, and augment public health initiatives by reducing the impact of the current pandemic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".