Management education of nurses-in-charge in Yukon / by Anne C. Dietrich Bragg.
Bibliographic record
Abstract
Recruitment and retention of skilled, qualified nurses to northern regions of Canada is an \non-going problem. One of many contributing issues may be the lack of management education \nafforded to the Nurses-In-Charge (NICs) in remote communities. If managers lack adequate \neducation, they cannot properly support the staff with whom they work; the environment of the \nhealth centre therefore deteriorates, then retention and recruitment of nurses becomes difficult. \nThis study investigates the type and frequency of management education given to NICs \nin the Yukon Territory. It also attempts to identify the type of education these NICs feel would \nbenefit them. Finally, a job satisfaction questionnaire attempts to determine how NICs feel about \ntheir work relationships and their job in general. \nBackground Information: \nFor the past decade, Canada has been experiencing a nursing shortage (Maslove & Fooks, \n2004, Office of Nursing Policy, 2005) which is expected to worsen during the next decade \n(Canadian Nurses Association, 2002). When there is a nursing shortage, outpost nursing stations \nand remote health centers suffer greatly, especially in First Nations communities; in 2001, there \nwas a reported vacancy rate of at least 40% on reserves, resulting in the closure of some nursing \nstations for lack of staff (Fletcher, 2001). This kind of shortage results in poor continuity of care \nto the detriment of patient well being (Minore et ah, 2005). Although nurse retention is a \ncomplex issue, especially in northern Canada where little research has been done (MacLeod, \nKulig, Stewart & Pitblado, 2004), overwork, burnout, and lack of management support and \nappreciation are some of the top reasons nurses cite for leaving northern areas (Tyler & Riggs, \n2000).
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".