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Record W2991745501 · doi:10.3390/ijerph16244921

Occupational Precariousness of Nursing Staff in Catalonia’s Public and Private Nursing Homes

2019· article· en· W2991745501 on OpenAlexaff
Ana Mari Fité-Serra, Montserrat Gea‐Sánchez, Álvaro Alconada-Romero, José Tomás Mateos, Joan Blanco‐Blanco, E. Barallat Gimeno, Judith Roca, Carles Muntañer

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersUniversitat de Lleida
KeywordsNursingPsychosocialMedicineTeam nursingScale (ratio)Nursing AssistantPrimary nursingOccupational health nursingWork (physics)Nurse educationNursing homesPublic healthHealth policy

Abstract

fetched live from OpenAlex

Nursing staff who provide care in the nursing homes of Catalonia have more precarious work conditions, including more demanding schedules and work overload, than those in other areas of care. This situation entails two major problems: Detrimental health results for nurses who face psychosocial and physical risks and a negative impact on the care provided to patients, with a decrease in the quality of care. This study aimed to describe the precarious employment situation of nursing staff in nursing homes. We carried out a descriptive study based on the employment precariousness scale (EPRES), which was administered to a sample of 239 nurses and nursing assistants working in public and private nursing homes in Catalonia. The highest level of job insecurity occurred among nursing assistants and in privately managed nursing homes. The precariousness of the working conditions of nursing staff poses a risk both to the workers themselves and to the people they tend to. For this reason, there is a need for greater knowledge on the scale of the problem and the implementation of appropriate legislative measures to alleviate it.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.132
GPT teacher head0.487
Teacher spread0.354 · 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 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

Citations28
Published2019
Admission routes1
Has abstractyes

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