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Deconstructing the Phenomenon of Apology

2017· article· en· W4402503002 on OpenAlexaffvenueabout
Amie Cameal Liddle

Bibliographic record

VenueJournal of Applied Hermeneutics · 2017
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsAlberta HealthAlberta Health Services
Fundersnot available
KeywordsPhenomenonEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.338
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2017
Admission routes3
Has abstractyes

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