Telementoring in Community Nursing: A shift from dyadic to communal models of learning and professional development
Bibliographic record
Abstract
This article reports on a six-month telementoring initiative in a Canadian community nursing organization. The way in which Internet technologies may support and augment face-to-face mentorship of health care professionals is a relatively unexplored area of research and was the focus of this project. Participants ( N =22) were all employees of Saint Elizabeth Health Care (SEHC), a community nursing agency servicing 150,000 clients in urban and rural Ontario, Canada. Nurse mentees ( n =11) and nurse-mentors ( n =11) engaged in collaborative discourse in webKnowledge Forum, a second-generation computer-supported intentional learning environment (CSILE). Discussions among all participants were directed at collaborative learning and professional development. Results indicate that mentees contributed and read more notes than mentors and were more likely to engage in threaded discourse with peers. Readership patterns were similar for both groups. Fifty-eight per cent of all nurses reported improved asynchronous communication and problem-solving skills as a result of online collaboration. Seventy-five per cent of all respondents reported a positive professional development experience and 50% of all respondents reported improved clinical practice ability as outcomes of the telementorship program. All reported high satisfaction with the technology. It is concluded that this project facilitated a shift from dyadic mentor-mentee (preceptor-intern) training to communal opportunistic learning and professional development.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".