Drug and poison information centres: An emergent need for health care professionals in Pakistan.
Bibliographic record
Abstract
OBJECTIVE: To determine the need of drug and poison information centres in public and private hospitals of Karachi. METHODS: The cross-sectional study was conducted at 3 public and 3 private tertiary care hospitals of Karachi, from July 2013 to April 2014, using a self-administered, multi-item questionnaire. Non-probability convenient sampling was used to select the participants. SPSS 18 was used to analyse data. RESULTS: Of the 307 physicians, 282(92%) highlighted the need for a 24/7 drug and poison information centre and 206(67%) suggested opening a drug information centre at the hospital. Besides, 215(70%) respondents said they took at least 15 minutes for searching information about the drug while managing a case. Regarding the poisoning case management, 160(52%) physicians complained about the unavailability of medicines in hospitals. CONCLUSIONS: Provision of 24 /7 drug information centres with specialised staff are necessary to reduce treatment delays and to ensure provision of quality healthcare.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".