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Record W4377093790 · doi:10.3389/fpsyg.2023.1155845

Varieties of suffering in the clinical setting: re-envisioning mental health beyond the medical model

2023· article· en· W4377093790 on OpenAlexaff
Paul T. P. Wong, D. A. Laird

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsTrent University
Fundersnot available
KeywordsConceptualizationExistentialismPsychologyMental healthPsychotherapistPsychological interventionCoping (psychology)Positive psychologyLonelinessPsychiatryEpistemology

Abstract

fetched live from OpenAlex

In this paper, we argue for the need to rethink mental health beyond the medical model because much of human suffering cannot be diagnosed by the DSM-5. During the pandemic and post-pandemic, people have learned to accept the fact that no one is immune from suffering. Given the universality and complexity of human suffering, it is natural for people to wrestle with existential questions such as "Why struggle when all life end in death?" and "How can one flourish when life is so hard?" Existential positive psychology (EPP or PP2.0) was developed to address these existential concerns. After explaining the inherent limitations of the medical model and the need for EPP as an alternative vision for mental health, we provide illustrative clinical cases to demonstrate the advantages of this broader existential framework for both case conceptualization and interventions. According to EPP, mental illness is reconceptualized as both deficiency in knowledge and skills in coping with the demands of life and deficiency in meeting the basic needs for livelihood and mental health, the Soul's yearnings for faith, hope, and love. Finally, we introduce integrative meaning therapy as a therapeutic framework which can equip people with the needed skills to achieve healing, wholeness, and total wellbeing.

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)0.008
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 score0.593
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.419
Teacher spread0.378 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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