Listening to and Learning from Graduates‘ Perceptions: Implications for Change?
Bibliographic record
Abstract
The impact of Word of Mouth Communication (WoMC) in attracting new candidates, and thus contributing to the sustainability of Higher Education Institutions, is highlighted in the literature along with its importance as an indicator for quality improvements in course provision. In the present study graduates were questioned concerning their intention to recommend Master courses depending on satisfaction, and tracking any specific characteristics or attitudes which formulate positive or negative WoMC after graduation. A total of 162 Master graduates in Health Management, academic years of admission 2003-2007, completed satisfaction questionnaires which were developed by the authors. WoMC was recorded through the relevant questions in the same questionnaire. Demographic, vocational, educational variables and a satisfaction score based on questionnaire, were tested, in order to measure the effect on strengthening WoMC, using simple and multiple logistic regression. Graduates, who showed high overall satisfaction level of the Master Program they had attended, adopted positive WoMC recommending it to interested parties (OR: 1.11, 95% CI: 1.05-1.17). In contrast, negative WoMC was adopted by those who were unemployed or who have been looking for work when surveyed, (OR: 0.12, 95% CI: 0.02-0.63). It is the first time that Health Management Master graduates of Greek public institutions have been surveyed concerning the effect of satisfaction along with individual characteristics (like sex, age, marital status, first obtained degree, employment status, work consistency with degree) on their intention to recommend the Master courses in question to others. The improvement of career prospects leading to positive WoMC can both attract new students enhancing sustainability of Master courses and also offer stakeholders among others a valuable indicator concerning improvements regarding educational quality.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".