Two-factor theory – at the intersection of health care management and patient satisfaction
Bibliographic record
Abstract
Josef BohmHealth Services Administration, New York City College of Technology, City University of New York, Brooklyn, NY, USAAbstract: Using data obtained from the 2004 Joint Canadian/United States Survey of Health, an analytic model using principles derived from Herzberg’s motivational hygiene theory was developed for evaluating patient satisfaction with health care. The analysis sought to determine whether survey variables associated with consumer satisfaction act as Hertzberg factors and contribute to survey participants' self-reported levels of health care satisfaction. To validate the technique, data from the survey were analyzed using logistic regression methods and then compared with results obtained from the two-factor model. The findings indicate a high degree of correlation between the two methods. The two-factor analytical methodology offers advantages due to its ability to identify whether a factor assumes a motivational or hygienic role and assesses the influence of a factor within select populations. Its ease of use makes this methodology well suited for assessment of multidimensional variables.Keywords: two-factor theory, behavioral theory, sociology of health care, patient satisfaction
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".