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Record W1950851204 · doi:10.1111/jep.12046

<scp>H</scp>eidegger, <scp>G</scp>estell and rehabilitation of the biomedical model

2013· article· en· W1950851204 on OpenAlexaff
Donald S. Borrett

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsToronto East General HospitalToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsTemporalityPerspective (graphical)OntologyMedicinePsychologyEpistemologyPsychotherapistPhilosophyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The biomedical model is the foundation upon which current evaluations in clinical practice are based. In the quest for objective evidence to support clinical interventions, the patient is reduced to a number of technologically generated variables that serve as a surrogate for the patient herself. The biomedical model, as a reflection of Gestell or the essence of technology, carries with it the danger that it may overwhelm the practitioner's perspective so that other perspectives that may include the lived experience of the patient are actively suppressed. An ontology of the patient based on a Heideggerian exegesis is developed as a response to this concern. Morris has suggested that the most fundamental disturbance in the lived experience of the patient is an alteration in the patient's relationship to ecstatic temporality. In ecstatic temporality, the past, present and future are experienced as a unity in which the patient sees herself as her own possibility. Access to this experience is disturbed in illness; the future is no longer experienced as the patient's own possibility but rather as a series of predetermined external events that dictate the patient's affairs. By developing a biologically plausible model of ecstatic temporality, the lived experience of the patient does not have to be considered separate from the physical mechanisms involved in the disease state. As a consequence, the biomedical model cannot suppress the practitioner's humane perspective since the latter is explicitly brought under its purview.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.095
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.005
Scholarly communication0.0070.003
Open science0.0020.003
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0950.027

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 head0.114
GPT teacher head0.441
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2013
Admission routes1
Has abstractyes

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