Therapeutic Communication for Health Care Administrators
Bibliographic record
Abstract
Welcome to Therapeutic Communication for Health Care Administrators Digital Text Open Educational Resources (OER). These resources are intended for learners preparing for positions in front-line health care settings. Recognizing the diverse titles for these types of roles, we intend that the title Health Care Administrator is an umbrella term that includes all types of front-line Health Care Administrators. This OER is intended to be used with the companion resources, Therapeutic Communications for Health Care Administrators Game Simulations to be released in Spring 2022.\nHealth Care Administrators are often the first point of contact for clients and their families and often are the liaison between the health care providers, and the client; thus, therapeutic communication is an essential competency. An environmental scan was sent to industry partners and colleagues in the Ontario higher education system in Spring 2020. Based on those results, it was identified that many existing resources do not address the communication skills of the health care administrator role. The digital text and companion game simulations address the gaps while also providing essential digital resources that can be used in remote, hybrid, or face-to-face delivery formats.\nThis digital text OER has been developed with Universal Design for Learning (UDL) elements in mind. The authors have purposefully used inclusive language such as they and them for singular pronouns in place of gendered pronouns. The cultural chapter components have been reviewed by members of the cultural communities addressed in the chapter while also recognizing that one or two reviewers do not represent entire cultures. We welcome your review and feedback and encourage you to reach out to the authors with any concerns, suggestions for modifications, and ideas for enhancements.
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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.113 | 0.048 |
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".