Comparison of the Institute for Credentialing Excellence Multi-Organization Survey of Certificants
Bibliographic record
Abstract
Abstract Introduction/Objective The Institute for Credentialing Excellence (ICE) conducted a study to measure the value that credential holders place on certification, collecting survey data from a large sample of more than 12,000 credential holders representing six organizations (4 of which were healthcare). In May of 2019, ASCP BOC sent the survey to 47,384 individuals certified within the last 5 years. Of those who received the survey; 2,038 completed the 80 required questions. A comparison of survey data provided an overview of the top five differences in the full survey data (n=9,407) and the data from respondents credentialed by ASCP BOC (n = 2,038 or 22% of full survey respondents) and highlighted the primary reasons individuals pursue certification. Methods/Case Report This survey sought to define certificants’ attitudes toward and experiences with certification. Survey questions were grouped by four primary subject areas: The Value of Certification for Respondents’ Professions The Value of Certification for Employers The Value of Certification Outside the U.S. and Canada The Value of Certification for Certificants - Responses of themed queries of full survey data and BOC survey data were viewed side by side and averages, standard deviations and medians were calculated. The top 5 items indicating the highest difference from the full survey data results were highlighted. - Ranked primary motivational factors for the pursuit of certification were viewed side-by-side. Results (if a Case Study enter NA) Based on a side-by-side comparison of full survey data and BOC survey data, only minor differences were noted with the exception of the question pertaining to the perceived value of recertification. Both differences and items of agreement were highlighted. The top 10 reasons for seeking certification were the same amongst both the full survey and ASCP BOC survey respondents, with minor differences in priority order. Conclusion With the exception of the question regarding the value of recertification, discrepancies noted were minor. On most questions pertaining to the various benefits of certification, a majority of respondents gave a positive response about certification and its relationship to beneficial outcomes for their professions, employers, or themselves. It is clear that certificants place a high value on certification and believe that certification provides a number of valuable benefits.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".