Bibliographic record
Abstract
Norhayati et al. (2017) stated that satisfaction is the perception of an individual’s experience compared to their expectation and with respect to patients, it is the extent to which their general health care needs, as well as their condition-specific needs, are met. Patient perspectives about the level of care can result in feedback useful for promoting higher quality standards of patient care (Anhang et al. 2014). A key element in the effectiveness of healthcare is the partnership between patients and healthcare providers (De Belvis et al. 2009; McGill & Felton 2007; Nam et al. 2011). Monitoring patients’ perception is an important strategy to improve the performance of healthcare professionals. The study assessed patient satisfaction among mine workers at a mine clinic. The study was grounded on qualitative methodology and adopt questionnaires, as data collection technique. Questionnaires were administered to participants as their come for their routine Covid 19 screening and routine clinic visits using Kobo data collection tool. It was suggested that relevant authorities to address feedback from patient satisfaction surveys which could assist with improved prioritisation and allocation of resources and it could also serve as a platform for providing better services and patient satisfaction surveys should be conducted periodically to keep the department abreast with current health care trends and practices in order to address emerging needs as noted by Fekadu, Andualem & Yohannes 2011; Mosadeghrad 2014).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".