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Record W7149898383

Réactions émotionnelles des médecins face aux patients suicidaires : influence des facteurs liés au médecin et au patient

2025· dissertation· en· W7149898383 on OpenAlexaboutno aff
Dimitrios Kiakos

Bibliographic record

VenueIRIS · 2025
Typedissertation
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Thematic analysisMental healthMental health carePoison controlSuicide preventionCountertransferenceSuicidal ideationMedical care
DOInot available

Abstract

fetched live from OpenAlex

Background The physician–patient relationship is essential in the care of suicidal patients, yet factors shaping this relationship remain insufficiently explored. This study aimed to explore physicians’ emotional reactions to suicidal patients and how both physician- and patient-related issues influence these responses. Methods Interviews were conducted with six physicians from the Douglas Mental Health University Institute in Montreal. A thematic analysis was performed using Hayes’ structural model of countertransference as the analytical framework. Results Three primary emotional reactions emerged: emotional connection/avoidance, confidence/doubts, and powerlessness attributed to own limitations/to the patient. Clinicians’ core needs—the need to help, need for security, and need for efficacy—were found to be pivotal in shaping these emotional responses. Similarly, patient-related factors, notably life experiences, disease, suicidality, and attitudes significantly influenced these reactions. Patterns linking physicians’ emotional responses to their underlying needs and patient-related factors were analyzed, leading to the development of a conceptual framework. Conclusions This framework offers implications for research, clinical supervision, and medical training, fostering deeper insight into the physician–patient relationship in the context of suicidality.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.047
GPT teacher head0.368
Teacher spread0.321 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueIRISSame topicSuicide and Self-Harm StudiesFrench-language works237,207