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Record W3085793462

Sleep quality, physical and psychological outcomes in nurses with low backpain from a tertiary hospital, South India

2020· article· en· W3085793462 on OpenAlexaboutno aff
Nirmala M Emmanuel

Bibliographic record

VenueMedical economics · 2020
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexMedicineAnxietyDepression (economics)Physical therapyOswestry Disability IndexLow back painPopulationInstitutional review boardSleep qualityPsychiatryInsomniaAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Low back pain (LBP) affects 80% of the population globally. In India, prevalence of LBP among nurses is reported to be 66%. Methodology: A descriptive cross-sectional study design was adopted to assess the sleep quality of nurses with low back pain in a tertiary care setting, South India and to determine the relationship of sleep quality with the physical and psychological parameters such as pain intensity, functional disability, anxiety, and depression. All the nurses willing to participate in the study, and available during the data collection period were screened for LBP. Among the nurses with LBP, 193 subjects were selected using systematic random sampling technique. Study was approved by the Institutional Review Board and informed written consent was obtained from the subjects. Subjects were asked to complete the following questionnaires: Pittsburgh Sleep Quality Index (PSQI), Short-form McGill Pain Questionnaire (SFMP), Oswestry Low Back Pain Disability Questionnaire (ODI), Zung Self-rating Anxiety (ZSA) and Depression (ZSD) scales. Results: Among 1284 nurses screened, 686 (53.4%) had LBP. Of the 193 nurses included in the study 68.4% of the nurses had good quality of sleep. Majority of the subjects had minimal disability (68.4%), moderate pain (81.3%), and normal anxiety (56.3%) and depression (91.7%) levels. There was a significant positive correlation between sleep quality and pain intensity (r=.355, p<.01), disability (r=.376, p<.01), anxiety (r=.297, p<.01), and depression (r=.233, p<.001). Conclusion: Improving sleep quality will decrease the physical and psychological manifestations of patients with low back pain and hence improve the quality of life of nurses with LBP. Biography: Nirmala M Emmanuel has completed her MSc Nursing at the age of 30 years from Christian Medical College (CMC), Vellore affiliated to Tamilnadu Dr. MGR Medical University. She is working as a Nurse Manager in the Surgical Nursing department of CMC, which is a multispeciality hospital with nearly 2500 beds. She also serves as a Professor at the College of Nursing, CMC, Vellore. Nursing is an integrated system of education and practice in the institution. Speaker Publications: 1. Cunningham C, Flynn T, Blake C. Low back pain and occupation among Irish health workers. Occup Med. 2006;56(7):23–28. 2. Mafuyai MY, Babangida BG, Mador ES, Bakwa DD, Jabil YY. The increasing cases of lower back pain in developed Nations: a reciprocal effect of development. AJIS. 2014;3(5):23–28. 3. Golob A, Wipf J. Low Back Pain. Med Clin North Am. 2014;98(3):405–428. 4. Lidgren L. The bone and joint decade 2000–2010. Bulletin of the World Health Organization. 2003;81(9):629. 5. CDC, author. Preventing back injuries in health care settings. Atlanta, USA: Centers for Disease Control and Prevention; 2008.   5th World Congress on Public Health and Nutrition; London, UK- February 24-25, 2020. Abstract Citation: Nirmala M Emmanuel, Sleep quality, physical and psychological outcomes in nurses with low back pain from a tertiary hospital, South India, Public Health 2020, 5th World Congress on Public Health and Nutrition; London, UKFebruary 24-25, 2020 (https://publichealth.healthconferences.org/abstract/2020/sleepquality- physical-and-psychological-outcomes-in-nurses-withlow- back-pain-from-a-tertiary-hospital-south-india)

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.298
Teacher spread0.283 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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