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Board 110 - Program Innovations Abstract Simulation in Healthcare Recruitment

2013· article· en· W2320673769 on OpenAlexaffabout
Joanne Azulay, Kathryn Parker, Tracey Millar

Bibliographic record

VenueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
Fundersnot available
KeywordsTeamworkInterviewInterpersonal communicationHealth carePsychologyCreativitySet (abstract data type)Medical educationApplied psychologyComputer scienceMedicineSocial psychologyManagementSociology

Abstract

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Introduction/Background Developed in 2001 by McMaster University, the Multiple Mini Interview (MMI) approach is an interview format that uses short independent assessments, typically in a timed circuit, to obtain an aggregate score of a candidate’s non-cognitive skills, such as interpersonal, professionalism and ethical/moral judgment. Holland Bloorview Kids Rehabilitation Hospital, Canada’s largest paediatric rehabilitation facility, piloted a new approach utilizing simulation as an innovative way to meet recruitment needs, satisfying the need for efficiency and focus on a client and family centred approach. Holland Bloorview’s approach was to adapt MMI principles, adding simulation and working with clients and families as partners in the decision making process. Methods The interview circuit was made up of eight stations (including two rest stations) and mini interviews lasted five minutes, with a two minute preparation period preceding the interview. Of these eight stations, two were designated simulation stations. Each station consisted of one interviewer and one rater. Stations evaluated previous work related experience, communication, interpersonal, teamwork, conflict resolution and decision making skills. Behaviour management, creativity, innovation and client engagement abilities were also evaluated, with a focus on the candidate’s ability to demonstrate a link between the desired skill set and a client and family centred approach. Simulation in healthcare recruitment is leading practice in healthcare. Conclusion Eighteen candidate interviews were completed within three hours - resulting in an 83% reduction in direct interview time in comparison to traditional interview processes and a 30% increase in efficiency related to pre and post interview activities. Offers were extended within five business days following the interviews and 100% of the vacancies were filled. The simulation stations were designed to reflect job relevant scenarios. Overall, interviewers and raters believed the simulation stations to be the best measure of the candidate’s ability to manage critical interpersonal challenges inherent to the position. In our upcoming evaluation, we will be looking for positive linkages to support simulation stations as valid predictors of successful performance. At Holland Bloorview, we value the knowledge and experience of our clients and family and encourage their input and participation in decision making. Clients participated as interviewers and raters and contributed a unique perspective to the selection process. The adapted MMI approach to recruitment with simulation exercises has fostered collaboration across peer groups, integration of clients and families in decision making, built positive energy for simulation and resulted in time efficiencies. For organizations that recruit and have limited resources, this approach can facilitate positive change and foster collaboration within a unique framework. References 1. Eva, K. W., Rosenfeld, J., Reiter, H. I. and Norman, G. R. (2004), An admissions OSCE: the multiple mini interview. Medical Education, 38: 314-326. 2. Lemay, J.-F., Lockyer, J. M., Collin, V. T. and Brownell, A. K. W. (2007), Assessment of non-cognitive traits through the admissions multiple mini-interview. Medical Education, 41: 573-579. Disclosures Holland Bloorview Kids Rehabilitiation Hospital.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.388
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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