Bibliographic record
Abstract
Within Alberta Health Services, the Alberta Provincial Patient Relations Department employs Patient Relations Consultants (PRCs) to assist unsatisfied patients, investigate healthcare related concerns, and facilitate resolution. The patients, who are referred to as complainants, interpret their experience and come forward with their complaint; the PRC is responsible to then interpret the complaint and take it forward for redress. In doing so, offering complainants an apology is unavoidable. Patient relations is an interpretive practice, however, and there are shortcomings when apology is inserted into the conversation. In this article, I deconstruct apology from a patient relations perspective. I draw upon concepts in Richard Kearney’s Strangers, Gods and Monsters (2003), as well as the work of Hans-Georg Gadamer and Jacques Derrida, to present an interpretive account of how the hospital is a host to strangers, and to patients. Following an unsatisfactory experience or adverse event, the patients become complainants, or monsters. The PRCs, who are also considered hosts, receive the monsters at their door and, in turn, they can become hostages to the monsters. In attempting to achieve “otherness” with the “monsters,” the phenomenon of apology is examined.
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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.140 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".