A Comparative study on the job description of medical record professional in selected countries and submit a model
Bibliographic record
Abstract
Introduction: Organization, given that is a dynamic, flexible social phenomenon, which constantly changes. Therefore, categorizing plan of jobs would not be a stable phenomenon. Therefore, because the content of job alters with technical advances, it had better to examine specially at the time of an annual assessment, the job description. In addition, the chief of medical records unit should, as possible as, predicate future changes. Methods: The present paper is a practical descriptive-analogical research, so that the researcher comprised available job descriptions structures of "Health Information Management Associations" by checklist fully. Then, the draft was designed based on that analogy, put on the table to be criticized by medical records experts. finally, the last proposal designee would be offered. Results: Job descriptions written by American Health Information Management enjoy the structure consists of job title, direct supervisor, main goal, responsibilities and competency. While Australian Health Information Association presents job descriptions comprising job code and class, unit name and supervisors as well as competency, and necessity conditions, and even times of work and rest. This association maintains to record performance indexes at the form of job description. Job description form, in Canadian Health Information Management Association is presented as assessment form of staffs, stating responsibility schedules and separated from job specification form. Conclusion: During double-stage screening of final draft, the researcher came to result that the job description form must include five dimensions as job title, its goal, its responsibilities, performance indexes, and job specification. The authorities believe that these features has the importance of 88%,82%,88%,87% ,91% , respectively.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".