A Quasi-Experimental Study To Assess The Effectiveness Of Structured Teaching Programme On Knowledge Regarding ABG Analysis And Its Interpretation Among NursingOfficers, BLK-MAX Super Speciality Hospital, New-Delhi.
Bibliographic record
Abstract
An arterial blood gas (ABG) is a blood test that measures the levels of many different gases in oxygen-rich blood.Some of these levels are measured directly while others are calculated from the measurements of other gases.ABG analysis is an essential investigation for Medical Surgical patients for diagnosis and managing patient's oxygen status and acid base balance.So it is necessary for all health care professionals to have adequate knowledge and skill to perform it.The aim of study was to assess the effectiveness of Structured Teaching Programme on Knowledge regarding ABG analysis and its interpretation among Nursing Officers, BLK-MAX Super Speciality Hospital, New-Delhi.Quantitative research approach was used with Quasi-experimental one group Pre-test Post-test design.Study population was nursing officers of BLK-Max Super Speciality Hospital New-Delhi.Convenience sampling technique was used to select sample.Sample size was 100 Nursing Officers.The data was collected through self-structured knowledge questionnaire.The findings of the study revealed that mean Post-test knowledge score 31.13 with Standard Deviation 2.926 was significantly higher than mean pre-test knowledge score 25.76 with Standard Deviation 3.400 as evident by paired 't' test (p value-<0.001*)at 0.05 level of significance.It showed that structured teaching programme was effective to enhance knowledge regarding ABG Analysis and its interpretation among nursing officers.Thus, the researcher concluded that structured teaching programme was highly effective to enhance the knowledge regarding ABG analysis and its interpretation among nursing officers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".