Canadian Nurse Practitioner Job Satisfaction
Bibliographic record
Abstract
PURPOSE: To examine the level of job satisfaction and its association with extrinsic and intrinsic job satisfaction characteristics among Canadian primary healthcare nurse practitioners (NPs). DATA SOURCES: A descriptive correlational design was used to collect data on NPs' job satisfaction and on the factors that influence their job satisfaction. A convenience sample of licensed Canadian NPs was recruited from established provincial associations and special-interest groups. Data about job satisfaction were collected using two valid and reliable instruments, the Misener Nurse Practitioner Job Satisfaction Survey and the Minnesota Satisfaction Questionnaire. Descriptive statistics, Pearson correlation and regression analysis were used to describe the results. CONCLUSIONS: The overall job satisfaction for this sample ranged from satisfied to highly satisfied. The elements that had the most influence on overall job satisfaction were the extrinsic category of partnership/collegiality and the intrinsic category of challenge/autonomy. These findings were consistent with Herzberg's Dual Factor Theory of Job Satisfaction. IMPLICATIONS FOR PRACTICE: The outcomes of this study will serve as a foundation for designing effective human health resource retention and recruitment strategies that will assist in enhancing the implementation and the successful preservation of the NP's role.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".