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Record W4283377029 · doi:10.3389/fpain.2022.903724

Lives Penciled in, the Reality of Chronic Health Conditions and Trauma: Reflexivity, Health, and Shadowed Identities

2022· article· en· W4283377029 on OpenAlexafffund
Richard Hovey, Veeresh Pavate, Marie Vigouroux

Bibliographic record

VenueFrontiers in Pain Research · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMontreal Children's HospitalMcGill University
FundersRéseau québécois de recherche sur la douleur
KeywordsMetaphorReflexivityCoping (psychology)Chronic painPsychologySocial psychologyPsychotherapistSociologyPsychiatry

Abstract

fetched live from OpenAlex

When living with chronic health conditions or experiences of trauma our lives can become perpetually penciled in. The use of the penciled-in metaphor means to arrange our time tentatively: a date, an appointment, a meeting, seeing a movie, or attending a class. In our technologically-driven world of electronic calendars where everything is entered electronically, the utility of the pencil and hand-written agendas have all but vanished. However, for the purpose of this article, the pencil provides a metaphoric common ground to learn about the totality of the disruption experienced by living with chronic health conditions and their residual trauma. The pencil is touchable, tangible and as a researcher and a person who lives with challenging health concerns, metaphors help me to create an understanding of the chaos of living a life in pain with cancer. This article is a person-centered account of the process of reflexive coping and self-processing of pain by a pain researcher and educator. This article focuses on the metaphor of penciled-in lives to provide a qualitative account of experiences of pain from chronic health issues and the trauma both physical and emotional it causes. This act of reflexivity becomes a personal examination of life. It reveals to me my beliefs, decisions, and practices before and during my hermeneutic journey and how these may have prejudiced my thinking and behaviors.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.132
GPT teacher head0.468
Teacher spread0.336 · 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 designQualitative
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
Published2022
Admission routes2
Has abstractyes

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