Academic success in five programs in allied health at the British Columbia Institute of Technology
Bibliographic record
Abstract
This study examined the nature and strength of relationship between specific related high school academic grades and the cumulative graduating average of students in five allied health programs at the British Columbia Institute of Technology. Lack of scientific studies on selection criteria for determining the cumulative graduating average of allied health professionals at the British Columbia Institute of Technology (B.C.I.T.) was evident. Educators argue that in order to enhance educational opportunities for institute students, there is a professional obligation upon the policy-makers to gather appropriate data to determine which factors contribute to the success of the allied health student. With the high cost of technical education, admission officers and admissions committees are accountable for their selection processes to the institute's administration, decision makers, provincial and federal funding sources, and society. The results of this study may assist admissions officers in selecting academic variables that indicate the cumulative graduating average so that a better match can be made between the students and their performance in allied health programs. The accessible population of 629 graduates from the allied health technologies in this study were biomedical electronics, medical laboratory, medical radiography, nuclear medicine, and prosthetics and orthotics. The dependent variable measurement of academic achievement for these students was their cumulative graduating average. Single variables consisted of the grade point average of the following: pretechnology academic requirements, high school English, high school algebra, high school biology, high school chemistry, and high school physics. Descriptive statistics, zero-order correlations, and stepwise multiple regression analysis were the statistical methods employed to determine which specific academic variable or multiple of variables exhibited a strong relationship between the cumulative graduating average and academic variables. The analysis identified certain variables that strongly related to the cumulative graduating average, both singly and in combination with others. Each of the program significant combination of variables are provided here in order of descending influence: Biomedical Electronics Technology- high school algebra; Medical Laboratory Technology- the pretechnology grade point average, high school chemistry, biology, and algebra; Medical Radiography Technology- high school biology and chemistry; Nuclear Medicine- the pretechnology grade point average, high school chemistry, and high school biology; Prosthetics and Orthotics Technology- the pretechnology grade point average and high school chemistry. Academic variables did not account for more than 34% of the total variables in any of the programs. The level of significance for individual variables was the convention, 0.05. Clearly, each program had its own character; however, the performance of students in the natural sciences were significant in four of the five programs. An attempt was made to investigate which specific high school subjects correlated highly with the cumulative graduating average of students at the B.C.I.T. through a inspection of five programs for five graduating classes. Relevant variables were identified that were indicative of academic achievement in each specific program of study. Investigating the nature and strength of relationship between preprofessional grades and the cumulative graduating average of allied health students at B.C.I.T. could benefit both students and admissions officers by supplying a piece to an educational puzzle that would demystify the selection process. The information presented may assist admissions officers and prospective allied health students make more suitable educational choices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".