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Record W4403831783 · doi:10.1681/asn.2024hwctbqtq

Provider Perspectives on Patient Burnout in Peritoneal Dialysis

2024· article· en· W4403831783 on OpenAlexaboutno aff
Spencer A. King, Karine Manera, Jenny I. Shen

Bibliographic record

VenueJournal of the American Society of Nephrology · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsPeritoneal dialysisBurnoutMedicineIntensive care medicineDialysisInternal medicineClinical psychology

Abstract

fetched live from OpenAlex

Background: Peritoneal dialysis (PD) can provide more independence and flexibility for patients compared with in-center hemodialysis (ICHD). However, this self-adminstered home-based modality can also cause burnout, which we define as mental, emotional, or physical exhaustion leading to negative attitudes towards PD. These attitudes can lead to poor outcomes, including depression, increased risk of peritonitis, and transfer to ICHD. We aimed to describe the perspectives on PD burnout by nephrologists with patients on PD. Methods: We conducted semi-structured interviews of 29 nephrologists with experience with treating patients on PD in Australia, Canada, Columbia, Hong Kong, Japan, New Zealand, Singapore, US, UK, Uruguay, and Thailand from Apr 2017 to Nov 2019. Transcripts were analyzed thematically. Results: Two major themes were identified that was similar to previous themes noted from a parallel study on patients’/carers’ perspectives on burnout. 1) Suffering an unrelenting responsibility: providers viewed their patients and carers being overwhelmed by the daily regimen and bearing alone the burden and uncertainty of what to expect from PD. 2) Adapting and building resilience: providers witnessed patients drawing hope and support from family and finding meaning in other activities. A third theme was coping with the aid of therapy: providers oberved patients and caregivers benefitting from meeting with a psychologist, psychiatrist, social worker and support groups, but also noted that such resources are not always readily accessible. Conclusion: Nephrologists with patients on PD are aware of burnout among their patients on PD and of means of coping. Further work is needed to identify effective ways for providers, patients, and families to openly communicate about burnout and to more broadly implement interventions to prevent it. Illustrative Quotes - Theme Quote Suffering an unrelenting responsibility "I think the long PD treatment can cause the patient to feel burnout. And the high frequency of the bag exchange by themselves, many times of the bag exchange cause patients feeling burnout.""If they find that they don't have enough time in their day to do their daily activities as well...they really burnout and they are really down in the dumps." Adapting and building resilience "And we see a lot of cases have caregiver burnout...But normally in Asian countries, I think we have a big family. So they can rotate to the other son, or daughter.""But I think having a family, children around for elderly is helpful." Coping with the aid of therapy "I think it would be good to have early psychological support or counseling if they, if we find that they're really struggling, because we can only do so much to help them, but we need to be able to refer them early on to a professional that can help, if they're feeling like they're starting to burn out or they're not coping with their diagnosis and their medical conditions, and the burden of debt. Because sometimes it hits them really hard, starting treatment.""And then we don't have a lot of resource. So we have one psychiatrists, but she just retired. And like every centers, we don't have that much help."

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)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.274
Teacher spread0.264 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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