The Effect of Information Technology on the Recruitment Process in Healthcare Organization in Makkah City
Bibliographic record
Abstract
As technology advances in recruitment and selection, it provides job seekers with more options for finding a career in the healthcare industry. At the same time, it helps organizations to streamline the hiring process. Internet job sites like CareerBuilder, Monster and Hot Jobs allow job seekers to post resumes which employers can view online and select qualified candidates. In addition, online candidate-tracking systems are helpful to employers. Social media forums like Facebook and LinkedIn are useful sources when selecting proper candidates for the healthcare industry. This research set aims to analyse the role of information technology in the healthcare recruitment process. The literature review demonstrates the impact of IT on the recruitment process in the healthcare sector. Literature from different secondary sources, such as books and journals, are discussed in this research. The methodology applied in this research takes a descriptive analytical and deductive approach. In the data collection and analysis part, the data were collected from 130 human-resource employees from different healthcare organizations in Makkah city. A quantitative data analysis method has been followed in order to analyse the raw data obtained in the survey. This helps to get appropriate outcome for the research by fulfilling its research aims and objectives. The outcome results from this study were as follows: First, that using information technology will be very useful in the recruitment process, because it will provide a better outcome than traditional methods. Second, there will be improvement in the quality of recruitment when using information technology in the recruitment process. Third, there will be a small improvement in communication when IT is used in the recruitment process rather than traditional methods. Finally, using IT in the recruitment process will be much more effective than the traditional way. The recommendations made from this study are the following: As outcome is one of the major components in the healthcare industry recruitment system, the applications for providing care must not vary between institutions. In order to improve quality in the healthcare recruitment process, it is necessary to apply IT in the proper way. For instance, those applications that have security issues and may lead to data leakage of candidates must be avoided. Using social media could be useful in enhancing communication in the healthcare recruitment process. The government has an important role to play in setting up standards for organizations. In turn, following these standards could be helpful in guaranteeing the effectiveness of the system.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".