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Record W2238297471

Investigate the Utilization of Natural Measures on relieving Post Cesarean Incision Pain

2014· article· en· W2238297471 on OpenAlexaboutno aff
Abdul Hanan, R Kamilia, Riyaz Ahmed, M Amina

Bibliographic record

VenueAsian Journal of Nursing Education and Research · 2014
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsMassageMedicinePhysical therapyMcGill Pain QuestionnairePain scaleSignificant differenceIntervention (counseling)NursingVisual analogue scaleAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Post cesarean section pain is a significant problem so this study aimed to investigate the utilization of natural measures on relieving post cesarean incision pain. The study design is an intervention study design. The study sample involved 150 mother divided into 75 mother as control group who received post cesarean section hospital routine analgesics for pain relief and 75 as intervention group who received foot and hand massage for 20 minutes. They were randomly selected from Ain Shams Maternity University hospital. Tools used for data collection were a structured interviewing questionnaire sheet, a numerical rating scale and short form McGill pain questionnaire. The results showed that, a statistically significant difference in mean of pain level among study groups at 6, 12, 18 hours after delivery, (p□0.00). Also there was a statistical significant difference between mean of pain score before and after massage immediately and one hour after massage. In the light of these results the study supports the effectiveness of foot and hand massage on relieving post cesarean section pain. So this study recommended that the intervention used in this study to be a booklet or brochure about pain management post cesarean section and distributed among maternity health services in Ministry of health Egypt.

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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.077
GPT teacher head0.430
Teacher spread0.353 · 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 designOther design
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

Citations4
Published2014
Admission routes1
Has abstractyes

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