Students' Perceived Self-Efficacy, Expectations, Barriers, and Support in Enrolling in a Master's Degree Program in Respiratory Care
Bibliographic record
Abstract
Background:There is an ever-present demand for increasing student enrollment in higher education institutions. Not surprisingly, respiratory care (RC) programs are under pressure to recruit and retain students in both undergraduate and graduate programs. The decision for an undergraduate student to continue their education into an advanced degree program is not fully appreciated. The purpose of this study is to determine students’ perceived self-efficacy, outcome expectations, barriers, and support to attend a Master of Science in Respiratory Care (MSRC) degree program. Methods:The survey developed by Crede and Borrego was modified to use in this study and included questions on undergraduate student self-efficacy, outcome expectations, perceived barriers, and support to attend the MSRC. The perceived impact of the MSRC on career goals was also explored. After receiving IRB approval and informed consent from all participants, 89 undergraduate students in the bachelor’s degree program at Texas State University were surveyed on campus. Results:A total of 87 surveys were collected (42 juniors and 45 seniors) with a response rate of 97%. Participant demographic results demonstrate 90% are female and the average age is 22.54±3.50 years. Self-efficacy results indicate 88.5% believe they would be successful if enrolled in the MSRC program. Only 58.6% have an interest in pursuing the MSRC possibly because 51.7% do not think the MSRC is required for the job they want. However, 98.8% believe the MSRC will create more opportunities for them. Cost (34%), tired of schoolwork (20%), and marriage (10%) are the three main reasons not to attend the MSRC. Also, 71.3% of participants do not know if they can receive financial support for tuition and living expenses in graduate school. Most participants (71%) do not know who to ask about attending graduate school, and 89% of participants think it will be hard to make friends in graduate school. Conclusions:RC students have self-efficacy to attend an MSRC program and believe it will provide more opportunities for them. However, the cost is the main barrier to enrolling in the graduate program and students do not know about resources available to them due to lack of support. This study highlights students’ perceived barriers and challenges in advancing their knowledge and continuing their education with an MSRC degree.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".