Emotional and Interpersonal Dimensions of Health Services
Bibliographic record
Abstract
Contributors examine the degree to which the provision of health care is influenced by characteristics of the health service organization, such as the administrative structure and the human resources available. They demonstrate that job satisfaction and conditions play an important role in shaping the quality and effectiveness of care and discuss the emotional support health care providers need to avoid long-term exhaustion and ensure well being. The contributors identify qualities of the client-provider interaction that lead to positive health care outcomes, such as providing information, responding to patient concerns, facilitating interactions with the health care system, and encouraging participation in personal health care and offer examples of innovative conceptual and analytical approaches to better health care practices. Contributors include Heather Boon (University of Toronto), Laurette Dubé, Carole A. Estabrooks (University of Alberta), Guylaine Ferland, Arlie Russell Hochschild (University of California, Berkley), Diane M. Irvine Doran (University of Toronto), Terrence Montague (Merck Frosst Canada), D.S. Moskowitz, Richard W.J. Neufeld (University of Western Ontario), Gilbert Pinard (McGill University), Debra L. Roter (John Hopkins Blooomberg School of Public Health), Dana Gelb Safran (New England Medical Center), and Krista K. Trobst (York University).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".