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Record W2915176448 · doi:10.13075/ijomeh.1896.01302

The impact of Balint work on alexithymia, perceived stress, perceived social support and burnout among physicians working in palliative care: a longitudinal study

2019· article· en· W2915176448 on OpenAlexaboutno aff
Ovidiu Popa-Velea, Liliana Veronica Diaconescu, C. Truţescu

Bibliographic record

VenueInternational Journal of Occupational Medicine and Environmental Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaBurnoutDepersonalizationEmotional exhaustionSocial supportPsychologyClinical psychologyToronto Alexithymia ScaleOccupational burnoutLongitudinal studyPalliative careMedicineNursingSocial psychology

Abstract

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Objectives: Physicians working with palliative patients have a substantial risk of emotional exhaustion because of their daily confrontation with suffering and death.Common concerns include alexithymia, high stress, low perceived social support and a greater burnout risk.This longitudinal study aimed to evaluate the effectiveness of Balint training in preventing the development of these symptoms in these medical professionals.Material and Methods: The design of the study was longitudinal.A group of 69 physicians working with palliative patients from 5 county hospitals in Romania (33 men, 36 women) participated in the study.Out of them, 31 joined and systematically attended a local Balint group whereas the others did not participate in such a group, either during the study or previously.They were given, both at the beginning (2015) and at the end of the study (2017), 4 psychometric instruments assessing alexithymia (Bagby's Toronto Alexithymia Scale), perceived stress (Cohen and Williamson's Perceived Stress Scale), social support (Duke-UNC Functional Social Support Questionnaire) and burnout (Maslach Burnout Inventory).A split-plot ANOVA analysis was used for evaluating the significance of Balint groups participation, with gender and age considered as auxiliary variables.Results: In the study group, Balint training significantly improved the scores of global burnout (F(1, 64) = 25.104,p < 0.0001), 2 of its components (emotional exhaustion (F(1, 64) = 18.390, p < 0.0001) and depersonalization (F(1, 64) = 10.957,p < 0.002), alexithymia (F(1, 64) = 3.461, p < 0.0001) and perceived social support (F(1, 64) = 57.883,p < 0.0001), but not the scores of perceived stress and low personal accomplishment.Gender had an additional contribution in decreasing alexithymia (F(1, 64) = 7.436, p < 0.009) and increasing perceived social support (F(1, 64) = 15.426,p < 0.0001), with higher effects in men.Conclusions: This study points to the potential usefulness of Balint training in addressing alexithymia and burnout, and in improving perceived social support among physicians working with palliative patients.As the Balint method is easily understood and does not require special investments, it could represent a cost-effective instrument of addressing job-related psychological risks.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.454
Teacher spread0.376 · 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 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".

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Citations33
Published2019
Admission routes1
Has abstractyes

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