Creación de una estrategia de marketing digital para el lanzamiento del software médico Companyon en personas que laboran de forma independiente en el área de la salud en Vancouver, Canadá
Bibliographic record
Abstract
El objetivo de este proyecto es diseñar una estrategia de marketing digital para la empresa CompanyOn, la cual ejecuta su operación en la ciudad de Vancouver en Canadá, con el fin de realizar el lanzamiento del software médico de gestión clínica pensado para el uso de los profesionales del área de la salud que laboran de manera independiente, para que puedan administrar y ejecutar su labor clínica de una forma simple y sencilla. A nivel metodológico se ha planteado una estrategia basada en el conocimiento del ecosistema digital, esta se basa en la generación de contenido de valor para el cliente por medio de los diferentes canales de medios digitales. Dicha estrategia tendrá una duración de seis meses, con el fin de permitir al consumidor interactuar con el contenido y generar tráfico al sitio web para producir acciones que se traduzca en engagement
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".