One year later: A case study examining a community hospital’s administrative response to a debris flow
Bibliographic record
Abstract
Santa Barbara, California was first impacted by the Thomas Fire in December 2017 only to be devastated by the Montecito Debris Flow less than two weeks later. Cottage Health, a not-for-profit health system that provides advanced tertiary and quaternary medical care for patients throughout the Central Coast of California, was at the forefront of first responders to those directly impacted by the debris flow. Santa Barbara Cottage Hospital (“SBCH”) (located 6 miles from the flow) treated 18 trauma patients, two first-responders, and 1 additional emergency patient later in the day, while Goleta Valley Cottage Hospital (“GVCH”) (located 12 miles from the flow) treated seven patients. Flooding and debris from the storm forced the closure of Highway 101 in both directions north and south for thirteen days. Highway 101 is the only major thoroughfare from Santa Barbara to Los Angeles and forced Cottage Health to implement an internal “travel agency,” in order to support Cottage Health clinicians to get to and from the two hospitals. The internal “travel agency” arranged transportation via planes, boats, vans and trains. In addition, the agency arranged accommodations utilizing local hotels, and an empty patient care unit in GVCH. Employees and board members opened up their homes to respond to more than 4,000 transportation and 900 overnight accommodation requests. Immediately following the disaster, licensed Cottage Health clinicians implemented a How We Heal: Trauma and Anxiety Support Group series to serve all Santa Barbara residents. The series is composed of How We Heal: Process Group, How We Heal: Skill Building/Seeking Safety Group and How We Heal: Survivor Group and one year later, continues to serve the community.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| 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".