Proposals for the Establishment of Social Service Departments at St. John's Hospital and Cowlitz General Hospital, Longview, Washington
Bibliographic record
Abstract
In partial fulfillment of the requirements for the degree of Master of Social Work from Portland State University, it was my desire to do a Practicum which would be of benefit to the community where I live, Longview, Washington. One area in which the shortcomings were apparent was pointed out to me time and again by my husband, who is a practicing physician in Longview and through my own contacts in the community, i.e., neither Cowlitz General Hospital nor St. John!s Hospital had a Social Service Department. Therefore, I have written proposals for a Social Service Department for each hospital. There are some slight variations, so that one proposal is applicable to St. John's Hospital and the other, to Cowlitz General Hospital. The proposal itself does not reflect the numerous contacts with people in the community, the subsequent efforts to implement the establishment of Social Service Departments, and the research and inquiries into available resources. My responsibility in doing a Practicum was to make a useful contribution to an existing agency, not to see to the actual establishment of the departments. This is, however, one of my personal goals. My list of the references, therefore, does not include all of the resources consulted in preparation for writing the proposals.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.105 | 0.020 |
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".