Information Needs of Post Myocardial Infarction (MI) Patients: Nurse's Perception in Universiti Kebangsaan Malaysia Medical Centre (UKMMC)
Bibliographic record
Abstract
Patient education has been considered as an integral component of care for patient after myocardial infarction. Post myocardial infarction patients require information and knowledge related to their conditions to reduce anxiety and aid recovery. The objective of this study was to identify nurses’ perception on information needs of post MI patients of UKMMC. A cross sectional study using Cardiac Patient Learning Needs Inventory (CPLNI) adopted from Timmins and Kalizer (2002) was used. It comprises seven cate-gories: related to anatomy and physiology, psychological factor, life style factor, medi-cation information, dietary information, physical activity, and symptom management. This study was conducted in coronary care unit (CCU), coronary rehabilitation ward (CRW), medical ward 1 and medical ward 2 from January 2007 to March 2007. 56 res-pondents (96%) who fulfilled the inclusion criteria were recruited in this study. Results showed that CCU and CRW respondents ranked symptom management as their first ranking followed by medication, life style factor, anatomy and physiology, dietary in-formation, physical activity and psychological factor. However, respondents in Medical Ward 1 and 2 reported and ranked medication information as their top priorities fol-lowed by symptom management, psychological factors, dietary information, physical activity, anatomy and physiology and life style factor. There were significant differ-ences between ward nurses’ responses with their perception related to anatomy and physiology, psychology factor, life style factor, physical activity, and symptom man-agement with p value<0.05. This study concluded that the information needs are not always perceived in unison by nursing personnel as the CCU and CRW nurses ranked symptoms management as the cardinal factor whilst Medical Ward 1 and 2 nurses give paramount importance to medication. In order to improve this situation for post MI pa-tient, information need has to be tailored, individualized and prioritized based upon their needs
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".