A qualitative interpretive description study exploring factors that influence emerging Nurse leaders on the path to leadership in Northern Manitoba
Bibliographic record
Abstract
The purpose of this study was to explore factors that shape emerging nurse leaders’ interest to step into a nurse leadership role in the Northern Health Region of Manitoba. Nurse leaders have a pivotal role in health care. Unfortunately, there is a relatively low proportion of nurses who plan to pursue nursing leadership roles, and an anticipated high number of nursing leaders expected to retire and leave the profession. A qualitative design using the approach of interpretive description was chosen to explore factors that influence emerging nurse leaders in northern Manitoba on their pathway to leadership. Using a modified version of Collings and Mellahi’s (2009) framework of strategic talent management, several factors were explored, including factors of work motivation, organizational commitment, and extra roles behaviour. This study also examined perception of organizational investment and the participant’s retrospective perceptions of experiential knowledge as it pertains to their interest in leadership. Based on the limited information available about talent management and nurse leaders in northern Canada, a purposive sample of 10 participants were recruited in northern Manitoba. Using a semi-structured interview guide, participants engaged in 1:1 virtual, one-hour interviews that were digitally recorded. Digital recordings were transcribed verbatim. Additional data source was a reflexive journal. Transcripts and journal were read and reread. Using constant comparison analysis, I identified three themes related to factors that influenced participants’ decision to move into formal leadership roles: i) relationships as the foundation of becoming a leader, ii) push and pull, and iii) the context of the north. Within the theme of relationships, supportive recognition and trust were described as crucial factors that motivated participants to consider leadership positions. Given the important role of the nurse leaders and the vital necessity of positioning our healthcare teams for success, it is imperative to understand the factors that shape emerging leaders. Talent management may provide a new lens through which to recruit and develop emerging nurse leaders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".