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Record W2763334264 · doi:10.1080/03069885.2017.1381665

Cures for the heart: a poetic approach to healing after loss

2017· article· en· W2763334264 on OpenAlexaff
Patricia A. McClocklin, Reinekke Lengelle

Bibliographic record

VenueBritish Journal of Guidance and Counselling · 2017
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAnguishFace (sociological concept)PoetryAestheticsPsychologyCompromiseMeaning (existential)FeelingPsychoanalysisSociologySocial psychologyEpistemologyLiteraturePhilosophyPsychotherapistArt

Abstract

fetched live from OpenAlex

Writing creatively, expressively, and reflectively to aid the grieving process is founded on the idea that in order to survive and thrive after loss, personal meaning must be made of what has been suffered. The individualisation and secularisation of society has put the onus of meaning making on the individual while an abiding reservation about speaking openly about death in Western culture complicates this task. In this article we examine how metaphors are used to make sense of loss and its ensuing emotions and how they help the writer move from a ‘first’ (i.e. anguish, pain) to a ‘second’ story (i.e. healing). We argue also that evoking the beloved through writing is part of healthy grieving. We conclude that nudging, thread and crystalising metaphors as well as ‘poetic conversations’ – that help locate and position oneself in relation to the loss – together make constructing new life-giving (i.e. second) stories possible. The safety and privacy of writing is also key in making space for the voice of the bereaved. We note that grieving individuals ‘write about’ and ‘write to’ and thus evoke the person who has died. In this paradoxical way the bereaved face the reality of death by retaining the loved one.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.340
Teacher spread0.304 · 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 designNot applicable
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

Citations7
Published2017
Admission routes1
Has abstractyes

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