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Record W2401581927 · doi:10.5539/ibr.v9n7p71

Workplace Stress in Comprehensive Health Centers and Its Impact on Career Commitment

2016· article· en· W2401581927 on OpenAlexvenueno aff
Naser Ibrahim Saif

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPsychologyDescriptive statisticsHealth careMedicineFamily medicineEnvironmental healthNursingStatisticsEconomicsMathematicsEconomic growth

Abstract

fetched live from OpenAlex

Considering that workers in healthcare institutions are most at risk of workplace stress (WPS), and that career commitment (CC) is a fundamental requirement for organizational success, this descriptive and quantitative survey study attempts to provide up-to-date details on the extent of WPS and CC and the impact of WPS on CC in Comprehensive Health Centers (CHCs) in Jordan during 2015 and 2016. Four hundred workers were randomly selected from twelve CHCs; the response rate was 73%. The study used the software SPSS version (15.0) for Windows to examine the data. The study produced a number of findings, with the results of mean and standard deviations revealing that the presence of WPS is high and CC is low among CHCs workers. Multiple regression tests showed that high levels of WPS have a negative impact on CC. The study results therefore suggest that appropriate interventions to control WPS may be useful to improve CC in CHCs in Jordan.

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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.484
Teacher spread0.360 · 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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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