A Descriptive Study of Employment Patterns and Work Environment Outcomes of Specialist Nurses in Canada
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The purpose was to describe the number, demographic characteristics, work patterns, exit rates, and work perceptions of nurses in Ontario, Canada, in 4 specialty classifications: advanced practice nurse (APN)-clinical nurse specialist (CNS), APN-other, primary healthcare nurse practitioner [RN(extended class [EC])], and registered nurse (RN) with specialty certification. The objectives were to (1) describe how many qualified nurses are available by specialty class; (2) create a demographic profile of specialist nurses; (3) determine the proportions of specialist and nonspecialist nurses who leave (a) direct patient care and (b) nursing practice annually; (4) determine whether specialist and nonspecialist nurses differ in their self-ratings of work environment, job satisfaction, and intention to remain in nursing. Employment patterns refer to nurses' employment status (eg, full-time, part-time, casual), work duration (ie, length of employment in nurses and in current role), and work transitions (ie, movement in and out of the nursing workforce, and movement out of current role). DESIGN: A longitudinal analysis of the Ontario nurses' registration database from 2005 to 2010 and a survey of specialist nurses in Canada was conducted. SETTING: The setting was Canada. SAMPLE: The database sample consisted of 3 specialist groups, consisting of RN(EC), CNS, and APN-other, as well as 1 nonspecialist RN staff nurse group. The survey sample involved 359 nurses who were classified into groups based on self-reported job title and RN specialty-certification status. METHODS: Data sources included College of Nurses of Ontario registration database and survey data. The study measures were the Nursing Work Index, a 4-item measure of job satisfaction, and 1-item measure of intent to leave current job. Nurses registered with the College of Nurses of Ontario were tracked over the study period to identify changes in their employment status with comparisons made between nurses employed in specialist roles and those registered as general staff nurses. Analysis involved descriptive summaries, mean comparisons with independent-samples t test, and χ(2) tests for categorical data. RESULTS: Exit rates from direct practice were highest for APN-other (7.6%) and CNS (6.2%) and lowest for RN(EC) (1.0%) and staff nurses (1.2%). χ(2) Tests indicated yearly exit rates of both APN-other and CNS nurse groups were significantly higher than those of staff nurses in all years evaluated (α = .025). Every specialist employment group scored significantly higher than staff nurses on measures of work environment and satisfaction outcomes. CONCLUSIONS: We provided a description of specialist nurses in Ontario and examined the relationship between specialization and employment patterns of nurses to inform nurse retention strategies in the future. Employment in specialist nursing positions is significantly associated with differences in transitions or exits from nursing among the specialist and nonspecialist groups. Registered nurses (EC) displayed improved retention characteristics compared with staff nurses. Advanced practice nurse-other and APN-CNS exit rates from nursing practice in Ontario were comparable to staff nurses, but exit rates from direct clinical practice roles were higher than those of staff nurses. IMPLICATIONS: Targeted strategies are required to retain CNS and APN-other in direct clinical practice roles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".